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# Data Literacy

Learn the Language of Data and AI

## Sitemaps

- [XML Sitemap](https://dataliteracy.com/sitemap.xml): Contains all public & indexable URLs for this website.

## Posts

- [Blog](https://dataliteracy.com/blog/) - Stay up to date on the latest news and announcements from the world of data literacy, and find helpful resources and tutorials to build your own skills.
- [Now Available: The Adaptive Organization](https://dataliteracy.com/now-available-the-adaptive-organization/) - Patrick McGarry's The Adaptive Organization: Leading Change in the AI Era is published today and ready to order in hardcover, paperback, and ebook.
- [New Book: The Adaptive Organization by Patrick McGarry](https://dataliteracy.com/new-book-the-adaptive-organization-by-patrick-mcgarry/) - The Adaptive Organization by Patrick McGarry is now available for pre-order on Amazon in hardcover and ebook. A practitioner's guide to data and AI leadership.
- [Introducing the Powered by Data Show](https://dataliteracy.com/pbd001/) - Introducing the Powered by Data Show Listen on Spotify, Apple Podcasts, YouTube, or wherever you get your podcasts! In this episode of The Powered by Data Show, Ben Jones and Alli Torban introduce the podcast’s new name and vision, exploring why the conversation around data has shifted from understanding to action. They discuss what’s holding
- [What is AI Literacy?](https://dataliteracy.com/what-is-ai-literacy/) - AI is expanding in use in the workplace, at home, and online, requiring individuals and organizations to develop AI Literacy to respond and adjust. But what is AI Literacy? In this blog post, we'll define AI Literacy and explore the four elements of its definition.
- [Season 02, Episode 02: Is Your Data Ready for AI? A Practical Self-Assessment (The Data Literacy Show)](https://dataliteracy.com/season-02-episode-02/) - Season 02, Episode 02: Is Your Data Ready for AI? A Practical Self-Assessment Listen on Spotify, Apple Podcasts, YouTube, or wherever you get your podcasts! In this episode of The Data Literacy Show, Ben Jones (CEO of Data Literacy) and Alli Torban (Senior Data Literacy Advocate) tackle a question they’re hearing everywhere: “Is our data
- [Beta Launch – Data Foundations for AI](https://dataliteracy.com/data-foundations-for-ai-beta/) - Take the Data Foundations for AI (DF4AI) beta to quickly assess whether your data is ready for AI - get a readiness score and clear next steps in about 10 minutes.
- [Season 02, Episode 01: Twelve Data & AI Predictions for 2026 (The Data Literacy Show)](https://dataliteracy.com/season-02-episode-01/) - Season 02, Episode 01: Twelve Data & AI Predictions for 2026 Listen on Spotify, Apple Podcasts, YouTube, or wherever you get your podcasts! In this episode of The Data Literacy Show, Ben Jones and Alli Torban share 12 predictions for data literacy in 2026, based on key lessons from the past year. They explore what
- [The AI Skills Gap](https://dataliteracy.com/ai-skills-gap/) - New research reveals 42% of employees expect AI to change their role within a year, yet 1 in 3 feel unprepared. Learn how to close your AI skills gap.
- [Our Best-Selling Books in 2025](https://dataliteracy.com/our-best-selling-books-in-2025/) - Our Best-Selling Books in 2025 As we welcome the new year, we want to share our top three best-selling books in 2025, in case you’re looking for a last-minute gift. 🙂 Happy Holidays, happy New Year, and happy reading! #1. AI Literacy Fundamentals Order on Amazon Author: Ben Jones What it’s About: To quickly get
- [Episode 12: Real Lessons from Building a Data Literacy Program with Neil Richards (The Data Literacy Show)](https://dataliteracy.com/episode-12/) - Episode 12: Real Lessons from Building a Data Literacy Program with Neil Richards Listen on Spotify, Apple Podcasts, YouTube, or wherever you get your podcasts! In this episode of The Data Literacy Show, Ben Jones (CEO of Data Literacy) and Alli Torban (Senior Data Literacy Advocate) talk with Neil Richards, an award-winning data visualization expert
- [Episode 11: Should We Use AI Agents? (The Data Literacy Show)](https://dataliteracy.com/episode-11/) - Episode 11: Should We Use AI Agents? Listen on Spotify, Apple Podcasts, YouTube, or wherever you get your podcasts! In this episode of The Data Literacy Show, Ben Jones (CEO of Data Literacy) and Alli Torban (Senior Data Literacy Advocate) share a practical framework to help you identify strong AI automation candidates, reduce risk, and
- [🛠️ We Can Build a Custom, On-Demand Course for Your Organization](https://dataliteracy.com/build-custom-course/) - 🛠️ We Can Build a Custom, On-Demand Course for Your Organization Are you looking to build data and AI literacy training in-house, but you’re unsure where to start? Lean on our expertise and course infrastructure! We’ll design and produce a custom, on-demand course for your organization so your people learn exactly what they need, when
- [Episode 10: Practical Steps for a Strong AI Strategy (The Data Literacy Show)](https://dataliteracy.com/episode-10/) - Episode 10: Practical Steps for a Strong AI Strategy Listen on Spotify, Apple Podcasts, YouTube, or wherever you get your podcasts! In this episode of The Data Literacy Show, Ben Jones (CEO of Data Literacy) and Alli Torban (Senior Data Literacy Advocate) share what makes a strong AI strategy and guide you through the first
- [Episode 9: Which Training Style is Best for Your Team? Pros/Cons of Top Modalities (The Data Literacy Show)](https://dataliteracy.com/episode-09/) - Episode 9: Which Training Style is Best for Your Team? Pros/Cons of Top Modalities Listen on Spotify, Apple Podcasts, YouTube, or wherever you get your podcasts! In this episode of The Data Literacy Show, hosts Ben Jones (CEO of Data Literacy) and Alli Torban (Senior Data Literacy Advocate) share the most common training styles (on-demand,
- [Our Top 10 Favorite Data Podcasts](https://dataliteracy.com/top-10-data-podcasts/) - Our Top 10 Favorite Data Podcasts We LOVE listening to podcasts here at Data Literacy. It makes household chores, commutes, and walks so much more enjoyable. It’s also such a gift to be able to learn about data in such a passive (and free) way. So! We’ve rounded up our top 10 favorite data podcasts
- [Episode 8: 3 Chart Swaps to Make Your Graphics More Effective (The Data Literacy Show)](https://dataliteracy.com/episode-08/) - Episode 8: 3 Chart Swaps to Make Your Graphics More Effective Listen on Spotify, Apple Podcasts, YouTube, or wherever you get your podcasts! In this episode of The Data Literacy Show, hosts Ben Jones (CEO of Data Literacy) and Alli Torban (Senior Data Literacy Advocate) share 3 smart chart swaps that instantly improve the clarity
- [Episode 7: A Better Way to Hire Data Freelancers and Consultants (The Data Literacy Show)](https://dataliteracy.com/episode-07/) - Episode 7: A Better Way to Hire Data Freelancers and Consultants Listen on Spotify, Apple Podcasts, YouTube, or wherever you get your podcasts! In this episode of The Data Literacy Show, hosts Ben Jones (CEO of Data Literacy) and Alli Torban (Senior Data Literacy Advocate) introduce their newest initiative: Data Freelancers, a vetted marketplace connecting
- [Episode 05: How to Analyze Data with the WISDOM Framework (The Data Literacy Show)](https://dataliteracy.com/episode-05/) - Episode 05: How to Analyze Data with the WISDOM Framework Listen on Spotify, Apple Podcasts, YouTube, or wherever you get your podcasts! In this episode of The Data Literacy Show, hosts Ben Jones (CEO of Data Literacy) and Alli Torban (Senior Data Literacy Advocate) introduce a structured, tool-agnostic framework for analyzing data: the WISDOM Framework.
- [Episode 6: What the Best Data Stories Have in Common (The Data Literacy Show)](https://dataliteracy.com/episode-06/) - Episode 6: What the Best Data Stories Have in Common Listen on Spotify, Apple Podcasts, YouTube, or wherever you get your podcasts! In this episode of The Data Literacy Show, hosts Ben Jones (CEO of Data Literacy) and Alli Torban (Senior Data Literacy Advocate) explore what the best data stories have in common. Drawing from
- [10 Things Every Business Leader Needs to Know About AI in 2025](https://dataliteracy.com/10-things-to-know-about-ai-in-2025/) - Discover the 10 essential things every business leader must know about AI in 2025—from risks and regulation to employee use, training strategies, and real-world applications. Stay ahead with practical, up-to-date insights that cut through the hype.
- [Data Storytelling for IMPACT is Finally Here!!](https://dataliteracy.com/data-storytelling-is-here/) - Our Data Storytelling for IMPACT Course is Finally Here! By Alli Torban, Senior Data Literacy Advocate at Data Literacy For years, I’ve seen people do incredible analysis, but then hit a wall when trying to communicate it. They’d end up hand-waving in front of their dashboard or cramming way too much on a slide. I’ve
- [5 Generative AI Pitfalls and How to Avoid Them](https://dataliteracy.com/5-generative-ai-pitfalls-and-how-to-avoid-them/) - Discover 5 critical generative AI pitfalls businesses face and practical strategies to avoid them. From over-reliance risks to AI literacy gaps, learn how to safely adopt AI tools while maintaining human oversight and preventing costly mistakes.
- [What the Best AI Literacy Programs Have in Common](https://dataliteracy.com/what-the-best-ai-literacy-programs-have-in-common/) - Discover the seven critical components of successful AI literacy programs, from tailored learning paths to responsible AI practices. Learn how leading organizations are preparing their teams for an AI-powered future.
- [The All-New Data Literacy Level 2 Course Is Here!](https://dataliteracy.com/the-all-new-data-literacy-level-2-course-is-here/) - Announcing the all-new Data Literacy Level 2 course—a powerful, tool-agnostic framework for analyzing data using the WISDOM Flow. Learn how to think critically with data and drive real-world impact through every step of the analysis process.
- [Webinar: Data Literacy — What I Wish My Leadership Had Known Earlier](https://dataliteracy.com/webinar-data-literacy-leadership/) - Webinar: Data Literacy — What I Wish My Leadership Had Known Earlier. Join Ben Jones, author of the new book Leading in the Age of Data, and Alli Torban as they break down the 7 factors that contribute to data-savvy leadership. We’ll share personal stories, common challenges, and offer actionable tips to help you lead with confidence and resonate positively with team members across all data literacy levels.
- [Episode 04: Create Better Data Stories by Testing Early and Often (The Data Literacy Show)](https://dataliteracy.com/episode-04/) - Episode 04: Create Better Data Stories by Testing Early and Often Listen on Spotify, Apple Podcasts, YouTube, or wherever you get your podcasts! In this episode of The Data Literacy Show, hosts Ben Jones (CEO of Data Literacy) and Alli Torban (Senior Data Literacy Advocate) introduce a smarter, iterative way to build data stories. They
- [Episode 01: How Organizations Can Measure Progress in Data & AI Literacy (The Data Literacy Show)](https://dataliteracy.com/episode-01/) - Episode 01: How Organizations Can Measure Progress in Data & AI Literacy Listen on Spotify, Apple Podcasts, YouTube, or wherever you get your podcasts! How do you know if your data and AI literacy program is actually working? In this inaugural episode of The Data Literacy Show, hosts Ben Jones (CEO of Data Literacy) and
- [Episode 02: The 3 Most Overlooked Traits of Data Literacy (The Data Literacy Show)](https://dataliteracy.com/episode-02/) - Episode 02: The 3 Most Overlooked Traits of Data Literacy Listen on Spotify, Apple Podcasts, YouTube, or wherever you get your podcasts! In this episode of The Data Literacy Show, hosts Ben Jones (CEO of Data Literacy) and Alli Torban (Senior Data Literacy Advocate) explore three essential—but often ignored—traits that separate truly data-savvy professionals from
- [Episode 03: What is Artificial Intelligence? (The Data Literacy Show)](https://dataliteracy.com/episode-03/) - Episode 03: What is Artificial Intelligence? Listen on Spotify, Apple Podcasts, YouTube, or wherever you get your podcasts! In this episode of The Data Literacy Show, hosts Ben Jones (CEO of Data Literacy) and Alli Torban (Senior Data Literacy Advocate) share the basics of Artificial Intelligence (AI). This is a super-simple primer to help you
- [Why True Data & AI Literacy Goes Beyond Technical Training](https://dataliteracy.com/why-true-data-ai-literacy-goes-beyond-technical-training/) - Discover why true data and AI literacy extends beyond technical skills, emphasizing ethical responsibility, critical thinking, continuous improvement, collaboration, and effective communication. Unlock deeper insights and lasting business success. 4.5
- [Launching Our Podcast: The Data Literacy Show](https://dataliteracy.com/data-literacy-show-trailer/) - Launching Our Podcast! The Data Literacy Show We’re so excited to announce that we launched a podcast! It’s called The Data Literacy Show — the podcast that helps organizations build, measure, and level up their data and AI literacy. It’s hosted by Ben Jones, co-founder and CEO of Data Literacy, and Alli Torban (me!), Senior
- [10 Must-Read AI Books](https://dataliteracy.com/10-must-read-ai-books/) - Discover 10 Must-Read AI Books recommended by Ben Jones, author of 'AI Literacy Fundamentals.' From technical insights to ethical debates, these recent classics are the best AI books that offer valuable perspectives for beginners and experts alike.
- [AI Literacy: The #1 Fastest Growing Skill in 2025](https://dataliteracy.com/ai-literacy-the-1-fastest-growing-skill-in-2025/) - AI Literacy tops LinkedIn's 2025 skills list. Learn what this means for your career, debunk common AI myths, and discover courses to build critical AI knowledge and judgment.
- [Data Literacy and AI Literacy: Two Sides of the Same Coin](https://dataliteracy.com/data-literacy-and-ai-literacy-two-sides-of-the-same-coin/) - Explore why data literacy and AI literacy are two sides of the same coin in today's digital world. Industry experts weigh in on why AI literacy is here to stay, what an effective AI literacy movement should include, and how data literacy serves as the foundation for AI competency. Discover the essential skills needed to thrive in an AI-driven future.
- [AI Literacy Training for Your Organization: Comply with the EU AI Act in 2025](https://dataliteracy.com/ai-literacy-training-for-your-organization/) - Prepare your organization for EU AI Act compliance with comprehensive AI literacy training. Learn AI fundamentals, generative AI, and responsible AI practices before enforcement begins August 2025.
- [How Organizations Can Measure Their Data & AI Literacy](https://dataliteracy.com/how-organizations-can-measure-their-data-ai-literacy/) - Learn how to measure your organization's data & AI literacy using four key dimensions: employee perceptions, knowledge assessment, implementation tracking, and value creation metrics.
- [NOW AVAILABLE: All-Access Subscription to Data & AI Literacy Training](https://dataliteracy.com/now-available-all-access-subscription-to-data-ai-literacy-training/) - Get unlimited access to expert-led Data & AI Literacy courses. Earn certificates, explore free trial lessons, and enhance your skills. Flexible plans for individuals, teams, and enterprises. Start learning today!
- [Announcing the Launch of 21 Key Traits of Data & AI Literacy](https://dataliteracy.com/announcing-the-launch-of-21-key-traits-of-data-ai-literacy/) - Boost your team's data & AI literacy! Our new course, 21 Key Traits of Data & AI Literacy, is here—available on-demand or through our new All-Access Subscription, which unlocks our entire course library.
- [Reflections on the 2nd Edition of Data Literacy Fundamentals](https://dataliteracy.com/reflections-data-literacy-fundamentals-2nd-edition/) - Ben's unfiltered thoughts about the revamping and republishing of what he feels is his most important book, Data Literacy Fundamentals: Understanding the Power & Value of Data.
- [Amazon Canceled, We Delivered: Get Your Ebook Now for Less!](https://dataliteracy.com/amazon-canceled-we-delivered-get-your-ebook-now-for-less/) - Amazon's delay resulted in a cancelation of all pre-orders of Data Literacy Fundamentals, 2nd Edition—but we’ve got you covered! Get the ebook TODAY, at a lower price, and in a flexible PDF format. Use code LEMONADE to save. Order now!
- [6 Must-Have Skills to Thrive as a Data Analyst in 2025](https://dataliteracy.com/6-must-have-skills-to-thrive-as-a-data-analyst-in-2025/) - Discover the 6 essential skills that set great data analysts apart in 2025. Beyond technical expertise, learn the core traits that drive success in data analysis and decision-making.
- [Is Your Organization Ready for EU AI Act Article 4? Essential AI Literacy Requirements for 2025](https://dataliteracy.com/is-your-organization-ready-for-eu-ai-act-article-4-essential-ai-literacy-requirements-for-2025/) - Learn how EU AI Act Article 4 mandates AI literacy training by 2025. Discover who needs to comply, what's required, and how to prepare your organization. Includes practical compliance solutions.
- [Now Available for Pre-Order: Data Literacy Fundamentals, 2nd Edition](https://dataliteracy.com/now-available-for-pre-order-data-literacy-fundamentals-2nd-edition/) - Discover the completely updated 2nd Edition of Data Literacy Fundamentals, featuring new AI literacy content, organizational implementation strategies, and professional illustrations. Pre-order the expanded guide that's helped countless professionals master data-informed decision making since 2020.
- [Announcing the New Data Literacy Score: Enhanced Assessment for the AI Era](https://dataliteracy.com/new-data-literacy-score-assessment/) - Discover how the enhanced Data Literacy Score—now with AI readiness assessment and objective skills validation—helps organizations measure and improve their data capabilities based on insights from thousands of professionals.
- [Insights Revealed: 5 Common Data Pain Points Today’s Organizations Face](https://dataliteracy.com/insights-from-the-data-literacy-score-team-based-assessment/) - On the 2 year anniversary of The Data Literacy Score Team-Based Assessment, Data Literacy CEO Ben Jones shares insights gleaned from the 2,000+ responses.
- [Announcing the Launch of Data Literacy Objective Assessments](https://dataliteracy.com/announcing-the-launch-of-data-literacy-objective-assessments/) - Skills-based objective assessments that test your level of data literacy knowledge.
- [Announcing the Launch of our Level 2 On-Demand Course!](https://dataliteracy.com/level-2-launch/) - In the third course in our Data Literacy training series, Data Literacy Level 2, you'll learn a new tool-agnostic framework for converting raw data into wisdo
- [Calling All Students, Educators and Data Literacy Advocates!](https://dataliteracy.com/calling-all-students-educators-and-data-literacy-advocates/) - Data Literacy for Education. Free & Discounted Resources for Teachers, Students, & Staff
- [Nominations are OPEN for the 2022 Data Literacy Awards!](https://dataliteracy.com/nominations-are-open-for-the-2022-data-literacy-awards-2/) - It's that time of year again! We'd like your help identifying the individuals and content that helped to advance Data Literacy the most this past year.
- [Voting is Now OPEN for the 2022 Data Literacy Awards!](https://dataliteracy.com/voting-is-now-open-for-the-2022-data-literacy-awards/) - We’re delighted to introduce the 2022 Data Literacy Awards shortlist candidates, chosen from among the nominations submitted by the general public over the past month. These exemplary candidates helped us speak the language of data even more fluently in 2022, and we’d like to congratulate all of them for being chosen from among their peers.
- [Announcing the Winners of the 2022 Data Literacy Awards!](https://dataliteracy.com/announcing-the-winners-of-the-2022-data-literacy-awards/) - We’re thrilled to announce the winners of the 2022 Data Literacy Awards, chosen by popular vote from among the shortlist candidates nominated by the general public. These exemplary winners helped us speak the language of data even more fluently in 2022, and we’d like to congratulate all of them for being awarded this distinction.
- [Do These 3 Classic Data Paradoxes Fool You?](https://dataliteracy.com/a-review-of-innumeracy-by-john-allen-paulos/) - A review of "Innumeracy: Mathematical Illiteracy and Its Consequences" by John Allen Paulos.
- [Explore Our YouTube Playlists!](https://dataliteracy.com/explore-our-youtube-playlists/) - Check out the latest videos shared on our YouTube channel, including resource reviews, overviews of crucial concepts, helpful tool tutorials, and "graphical gaffes" - examples of challenging charts and graphs that might be misleading or confusing.
- [Breaking Down the 4 Different Levels of AI: From Narrow AI to General AI to Superintelligence](https://dataliteracy.com/breaking-down-the-4-different-levels-of-ai/) - Breakdown of the 4 levels of AI - ANI (Artificial Narrow Intelligence), GPAI (General Purpose Artificial Intelligence), AGI (Artificial General Intelligence), and ASI (Artificial Super Intelligence).
- [ChatGPT 4o: Exploring the New Interactive Data Table Functionality](https://dataliteracy.com/chatgpt-4o-exploring-the-new-interactive-data-table-functionality/) - Explore what OpenAI's new interactive data table functionality in ChatGPT means for data analysts.
- [Introducing Our New Course: Harnessing Generative AI](https://dataliteracy.com/introducing-our-new-course-harnessing-generative-ai/) - Our next book, AI Literacy Fundamentals, is now available to purchase in paperback or digital forms! You can buy the paperback on Amazon, a digital copy for your Kindle, or an interactive PDF right here on our own online store.
- [Why Sora Struggles With Real-World Physics](https://dataliteracy.com/sora-real-world-physics/) - Ben Jones explores how OpenAI's Sora video generation model can struggle with basic physics, revealing the current limitations of AI in understanding and simulating the real world.
- [Our Best-Selling Books in 2024](https://dataliteracy.com/best-selling-books-2024/) - As we bid farewell to 2024 and welcome the festive season, we're celebrating our year of incredible learning achievements! At the top of our best sellers list...
- [Happy 6th Birthday, Data Literacy!](https://dataliteracy.com/happy-6th-birthday-data-literacy/) - Celebrating 6 years of Data Literacy! From navigating the COVID-19 pandemic to leading the AI literacy movement, discover how our small but mighty team has thrived, adapted, and grown to empower businesses and individuals worldwide. Learn about our journey and future plans to close the data and AI literacy gap.
- [NOW AVAILABLE: Advancing Responsible AI](https://dataliteracy.com/now-available-advancing-responsible-ai/) - Find out about the third installment in our AI Literacy series, Advancing Responsible AI. This course will teach you to evaluate, implement, and advocate for ethical AI practices.
- [Data Literacy for U.S. Voters, Part 5: All Eyes on the Swing States](https://dataliteracy.com/data-literacy-for-u-s-voters-part-5/) - Data Literacy for U.S. Voters Part 3: All Eyes on the Swing States This is the fifth part of our blog post series, Data Literacy for U.S. Voters. In the first part, we touched on the timing of U.S. elections, from the White House all the way down to your local school board. In the
- [Beyond the Map: How Marimekko Charts Offer a New Perspective on Election Results](https://dataliteracy.com/beyond-the-map-how-marimekko-charts-offer-a-new-perspective-on-election-results/) - Beyond the Map: How Marimekko Charts Offer a New Perspective on Election Results Written by Jon Schwabish for The PolicyViz Newsletter Note: this blog post serves as the fourth in our Data Literacy for U.S. Voters series. Maybe the most common data visualizations to show election results are maps. Maps, maps, maps. People love maps. The
- [COMING SOON: Advancing Responsible AI](https://dataliteracy.com/coming-soon-advancing-responsible-ai/) - Find out about the third installment in our AI Literacy series, which will launch soon. This course will teach you to evaluate, implement, and advocate for ethical AI practices. Join the waitlist for launch week discounts.
- [Data Literacy for U.S. Voters, Part 3: Understanding Election Polls](https://dataliteracy.com/data-literacy-for-u-s-voters-part-3/) - Explore the world of election polls: Learn about types, methods, and key statistical concepts. Understand margin of error, sampling, and how to interpret poll results accurately. Essential guide for voters and political enthusiasts.
- [Data Literacy for U.S. Voters, Part 2: Determining Winners and the Electoral College](https://dataliteracy.com/data-literacy-for-us-voters-part-2/) - Explore the U.S. Electoral College system in this helpful primer. Learn how presidents are elected, understand popular vote vs. electoral votes, and discover key misconceptions. Ideal for voters seeking clarity on America's unique election process.
- [Data Literacy for U.S. Voters, Part 1: Timing of U.S. Elections](https://dataliteracy.com/data-literacy-for-us-voters-part-1/) - Discover the rhythm of U.S. elections in this comprehensive guide. Learn about federal, state, and local election timing, primaries, and special elections. Understand how the election calendar impacts voter engagement and policy-making. Essential reading for data-savvy voters seeking to navigate the complexities of American democracy.
- [AI Training Navigator: What to Ask When Choosing the Right AI Training
for Your Team](https://dataliteracy.com/ai-training-navigator/) - Keeping pace with the rapidly evolving Artificial Intelligence (AI) landscape is a massive challenge, especially when you need to spearhead the search for the right training for your team. To make this task easier, we've compiled a list of questions you and your team should consider as you explore your AI training options.
- [Now Available for Pre-Order: AI Literacy Fundamentals](https://dataliteracy.com/ai-literacy-fundamentals-preorder/) - Ben Jones's next book is available for pre-order on Amazon! AI Literacy Fundamentals is for anyone who wants to join the AI conversation to help steer these transformational and controversial technologies into a future that will benefit us all.
- [AI Literacy Fundamentals Hits the Shelves!](https://dataliteracy.com/ai-literacy-fundamentals-hits-the-shelves/) - Our next book, AI Literacy Fundamentals, is now available to purchase in paperback or digital forms! You can buy the paperback on Amazon, a digital copy for your Kindle, or an interactive PDF right here on our own online store.
- [Announcing the Launch of AI Literacy Fundamentals](https://dataliteracy.com/ai-literacy-fundamentals-launch/) - Find out about our newest course, AI Literacy Fundamentals, and how it can give you and your team the ability to join the AI conversation affecting your work and your world. We should all have a voice in how AI gets used and how it doesn't get used. Now is the time to educate ourselves so we can participate and shape the future together.
- [How to Be the Ultimate Brainstorming Partner Without a Single Good Idea](https://dataliteracy.com/ultimate-brainstorming-partner/) - What's the difference between wide and long format data? Why does it matter? How can you pivot from one format to another? We explain it all in this video!
- [Data Literacy: Our Secret Sauce](https://dataliteracy.com/data-literacy-our-secret-sauce/) - In this celebration of the 5th birthday of Data Literacy, CEO Ben Jones unveils new company branding and provides a vision of what's coming in the next 5 years of the company and movement.
- [Explained: Wide vs Long Format Data](https://dataliteracy.com/wide-long-data-format/) - What's the difference between wide and long format data? Why does it matter? How can you pivot from one format to another? We explain it all in this video!
- [Unveiling Our Revamped Data Literacy Fundamentals Course!](https://dataliteracy.com/unveiling-data-literacy-fundamentals-revamped/) - Unveiling Our Revamped Data Literacy Fundamentals Course! Today we’re announcing the launch of our newly revamped Data Literacy Fundamentals course – a comprehensive learning experience designed to help you lay a firm foundation of data knowledge and skills. With interactive content, engaging delivery, and an eye on practical application, this course is the ideal way
- [A Brand Refresh for our 5th Birthday!](https://dataliteracy.com/a-brand-refresh-for-our-5th-birthday/) - In this celebration of the 5th birthday of Data Literacy, CEO Ben Jones unveils new company branding and provides a vision of what's coming in the next 5 years of the company and movement.
- [Coming Soon: "Chart Spark" by Alli Torban](https://dataliteracy.com/coming-soon-chart-spark/) - Introducing "Chart Spark" by Alli Torban. Harness your creativity in data communication to stand out and innovate. Available as an ebook for pre-order on Amazon. Paperback coming Dec. 5th, 2023.
- [An Illustrated Guide to Data Literacy: Make a Goal Tree](https://dataliteracy.com/an-illustrated-guide-to-data-literacy-make-a-goal-tree/) - Welcome back to our comic series, An Illustrated Guide to Data Literacy by Alli Torban! In this episode, Becky and Fern learn a new way to break down a big goal.
- [NOW OFFERING: Data Literacy Solutions](https://dataliteracy.com/now-offering-data-literacy-solutions/) - Announcing our brand new SOLUTIONS offerings - BI Consulting, Data Literacy Program Strategy, Creative Visualization Design, and Data Leadership Coaching & Mentoring. Tap into our expertise to achieve your goals in the coming year!
- [From Egypt to the U.S.: What I Learned During my Data Internship Abroad](https://dataliteracy.com/egypt-to-us-data-internship-abroad/) - Guest post from Dina Teilab of Egypt where she shares the top three things she learned during her internship at Data Literacy as part of the 2023 Professional Fellows Program (PFP) sponsored by the U.S. State Department and implemented by CRDF Global .
- [An Illustrated Guide to Data Literacy: What's an Interval Scale?](https://dataliteracy.com/an-illustrated-guide-to-data-literacy-interval-scale/) - Welcome back to our comic series, An Illustrated Guide to Data Literacy by Alli Torban! In this episode, Becky and Fern learn what an interval scale is and why it matters.
- [Bing Chat's Powerful but Flawed Chart Reading Capabilities](https://dataliteracy.com/bing-chat-chart-interpretation/) - Bing Chat now has the ability to recognize images, but can it read and interpret charts? Ben Jones reviews this new functionality, and gives the groundbreaking LLM based on OpenAI's GPT-4 four different charts to test it out.
- [The Data Leadership Maturity Model](https://dataliteracy.com/the-data-leadership-maturity-model/) - In this 3rd video in the "Crucial Concepts" series, Data Literacy CEO Ben Jones shares his "5 Stages of Maturity of the Data Literate Executive." How do some leaders evolve and ascend higher and higher to become wiser in their application of data, and how do some leaders descend into data toxicity?
- [NEW COURSE! Data Literacy for Leaders](https://dataliteracy.com/new-course-data-literacy-for-leaders/) - Discover the Era of Data-Informed Leadership: Enroll in "Data Literacy for Leaders" Course Today! Learn to make data-driven decisions while upholding values and vision. Use discount code DL4L50 for 50% off in September 2023.
- [New Release: "Leading in the Age of Data" by Ben Jones](https://dataliteracy.com/new-release-leading-in-the-age-of-data-by-ben-jones/) - Introducing "Leading in the Age of Data" by Ben Jones: A transformative guide for leaders in the digital age, focusing on ethics, culture, and team empowerment. Get actionable strategies and self-assessment tools to harness the power of data. Available now as an ebook on the Data Literacy store.
- [Now Available for Pre-Order: Leading in the Age of Data](https://dataliteracy.com/now-available-for-pre-order-leading-in-the-age-of-data/) - Pre-order Ben Jones' new book, Leading in the Age of Data, now available on Amazon! Discover the power of data-savvy leadership and how it can transform your organization for success. Don't miss out on this impactful read!
- [Data Literacy for Kids!](https://dataliteracy.com/data-literacy-for-kids/) - Discover Data Literacy for Kids: A book by Kathleen Yu that aims to inspire young students to embrace the power of data and make informed decisions. All proceeds go to FeedingCreativity.org, a nonprofit that fosters creativity in young minds. Buy now on Amazon!
- [Summer School Sale!](https://dataliteracy.com/summer-school-sale/) - Make the most of your summer downtime with our Summer School Sale - 25% off our entire data course library. Learn essential data skills and boost your confidence in interpreting and working with data. Don't miss out, sale ends on September 22nd!
- [Resource Review: Nightingale Magazine by DVS](https://dataliteracy.com/resource-review-nightingale-magazine-by-dvs/) - Resource Review: Nightingale Magazine In our latest video in the Resource Reviews series on our Data Literacy video channel on YouTube, our CEO Ben Jones gives you a sneak peek at the latest issue of the Nightingale Magazine that’s published biannually by the Data Visualization Society, and he talks about why he’s such a big fan
- [NEW COURSE: Working with Data Professionals](https://dataliteracy.com/launch-working-with-data-professionals/) - ANNOUNCEMENT! The course and ebook “Working with Data Professionals (even if you aren’t one!)” by Anna-Maria Steverson is LIVE and available to purchase!
- [Trying out the Noteable Plugin for ChatGPT Plus](https://dataliteracy.com/trying-out-the-noteable-plugin-for-chatgpt-plus/) - In this blog post, Ben Jones walks you through the new Noteable plugin for ChatGPT Plus, and shows you how this combination of LLM and computing notebook can revolutionize data analytics
- [Happy Birthday, Data Literacy! Reflections on Our 3rd Year](https://dataliteracy.com/happy-birthday-data-literacy-reflections-on-our-3rd-year/) - A look back at the biggest developments and milestones of our 3rd full year of operation as a small business helping people to learn the language of data, including course launches, team member additions, partnership announcements, and more.
- [Now Available for Pre-Order: Working with Data Professionals](https://dataliteracy.com/pre-order-working-with-data-professionals/) - ANNOUNCEMENT! As of today, the ebook “Working with Data Professionals (even if you aren’t one!)” by Anna-Maria Steverson is available for pre-order on Amazon. Pre-order your copy today!
- [An Illustrated Guide to Data Literacy: Use Your Intuition](https://dataliteracy.com/an-illustrated-guide-to-data-literacy-use-your-intuition/) - Welcome back to our comic series, An Illustrated Guide to Data Literacy by Alli Torban! In this episode, Becky discovers why intuitive thinking is just as important as analytical thinking!
- [An Illustrated Guide to Data Literacy: Bias in Research](https://dataliteracy.com/an-illustrated-guide-to-data-literacy-bias-in-research/) - Alli Torban is back with another data comic strip! Follow characters, Becky and Fern, as they figure out an unbiased way to poll their coworkers.
- [An Illustrated Guide to Data Literacy: Base Rate Fallacy](https://dataliteracy.com/an-illustrated-guide-to-data-literacy-base-rate-fallacy/) - Alli Torban is back with another data comic strip! Follow characters, Becky and Fern, as they try to avoid falling for the base rate fallacy.An Illustrated Guide to Data Literacy: Base Rate Fallacy Welcome back to our comic series that explores a range of data literacy concepts. We follow Becky, who describes herself as a bit data-phobic, as she navigates tricky situations at her new job. She’ll learn to create effective charts, grasp statistical concepts, and confidently clean data
- [An Illustrated Guide to Data Literacy: Percent Change + Percentage Points](https://dataliteracy.com/an-illustrated-guide-to-data-literacy-percent-change-percentage-points/) - Alli Torban is back with another data comic strip! Follow characters, Becky and Fern, as they figure out the difference between a percent change and percentage points.
- [An Illustrated Guide to Data Literacy: When Small Changes Make a Big Difference](https://dataliteracy.com/an-illustrated-guide-to-data-literacy-small-changes/) - Data Literacy comic series that explores a range of data literacy concepts. We follow Becky, who describes herself as a bit data-phobic, as she navigates tricky situations at her new job.
- [An Illustrated Guide to Data Literacy: Data is Useless on its Own](https://dataliteracy.com/an-illustrated-guide-to-data-literacy-data-is-useless-on-its-own/) - An Illustrated Guide to Data Literacy: Data is Useless on its Own Welcome back to our comic series that explores a range of data literacy concepts. We follow Becky, who describes herself as a bit data-phobic, as she navigates tricky situations at her new job. She’ll learn to create effective charts, grasp statistical concepts, and
- [Trying Out Code Interpreter for ChatGPT](https://dataliteracy.com/code-interpreter-for-chatgpt/) - Get a sneak peek at how the Code Interpreter plugin for OpenAI's ChatGPT can revolutionize data analysis. Upload data, submit prompts, and see ChatGPT generate python to analyze and visualize the data
- [How Making Data Physical Could Help Us Care for the Planet](https://dataliteracy.com/making-data-physical/) - How Making Data Physical Could Help Us Care for the Planet In celebration of World Earth Day, we have a special guest post by data journalist Miriam Quick. This story was originally published in Nightingale. The city of Sheffield, UK, has long had a reputation for appalling air quality. Dominated by the smokestacks of steel
- [ChatGPT + Wolfram: A First Look](https://dataliteracy.com/chatgpt-wolfram-a-first-look/) - It seems the marriage of statistical AI (LLMs such as OpenAI's ChatGPT) and symbolic AI (rules-based programming languages such as Wolfram Language) is poised to revolutionize how we perform some of the tasks of data analysis and data visualization, as well as how we go about describing the results of that analysis using everyday language. Based on my early exploration of the Wolfram plugin to ChatGPT, currently in alpha, I believe this will have a major impact in how we interact with data, and what it means to be highly data literate.
- [Webinar Replay: Fireside ChatGPT](https://dataliteracy.com/fireside-chatgpt/) - Webinar Replay: Fireside ChatGPT Last week, half of the Data Literacy team — Ben Jones and Alli Torban — hopped on a livestream to chat about our concerns with ChatGPT along with some of the interesting applications we’ve discovered. Watch the replay below if you’re interested! Here are the main highlights: Be aware of privacy
- [Here's the ChatGPT prompt that reduced my meal-planning time by 90%](https://dataliteracy.com/chatgpt-prompt-reduce-meal-planning-time/) - Discover how ChatGPT revolutionized my meal planning, reducing my planning time by 90% and tackling numerous constraints with ease. Read about the customizable prompt template I use to simplify meal planning and get inspired to apply AI to other personal tasks in my life.
- [NEW E-BOOK! ChatGPT Basics: Exploring Its Origins, Uses, and Misuses](https://dataliteracy.com/new-book-chatgpt-basics/) - Today, we’re announcing the launch of the ChatGPT Basics e-book, authored by our Co-Founder and CEO Ben Jones. Ben has been immersing himself in various iterations of this tool and interfacing with industry experts and developers to learn how to use it and to deploy it on our site. He has captured his learnings to date in this short e-book that will take about an hour of your time to read.
- [NOW AVAILABLE - ChatGPT Basics: Foundations & Getting Started](https://dataliteracy.com/chatgpt-basics-course-launch/) - Learn to use ChatGPT, the powerful new AI chatbot by OpenAI, in just 1-2 hours with ChatGPT Basics: Foundations and Getting Started. Get a better understanding of what ChatGPT is, how to use it, and how not to use it. The course features five lessons that take you through the content step-by-step and includes quizzes at the end of each lesson. Upon completion, you'll receive a certificate of completion and a badge to add to your LinkedIn profile page. Give it a try today and interact with ChatGPT in a variety of places!
- [Interview with an Expert: 5 Key Cybersecurity Insights for Businesses](https://dataliteracy.com/cybersecurity-insights-for-businesses/) - At Data Literacy, we take our customers' data security seriously and have worked with cybersecurity firm Secure Cloud Innovations for over a year. Their expertise impressed us so much that we had to interview co-founder Caleb Mattingly. During our conversation, he shared valuable insights on a fast-growing cyber threat, common traits of companies that respond well to attacks, and more…
- [Introducing Our Newest Team Member, Alli Torban!](https://dataliteracy.com/introducing-alli-torban/) - Today I have the honor and pleasure of announcing that we have just hired award-winning Information Designer Alli Torban! Alli joins our team as our Sr. Data Literacy Advocate.
- [Spatial Data is Messy - How to Make Peace with It](https://dataliteracy.com/spatial-data-is-messy-how-to-make-peace-with-it/) - Mapping expert Sarah Battersby covers four common problems that surface when working with spatial data, so that you can work to ensure that your analyses and visualizations are accurate and reliable.
- [A Chat with the Creator of the Information Graphic Visionaries Series](https://dataliteracy.com/a-chat-with-the-creator-of-the-information-graphic-visionaries-series/) - Listen along as Data Literacy CEO Ben Jones chats with expert data storyteller RJ Andrews of Info We Trust about Information Graphic Visionaries, RJ's brand new 3-book series showcasing the work of spectacular pioneers in data visualization.
- [What You Need to Know About Body Mass Index](https://dataliteracy.com/what-you-need-to-know-about-bmi/) - THERE IS NO “PERFECT WEIGHT” that applies to all of us. Body Mass Index (BMI) measures how healthy your weight is, based on your height. BMI has become a standard health assessment tool in my radiation oncology office and many healthcare facilities. But is BMI outdated?
- [Patient and Persuasive: How Florence Nightingale conveyed data insights to all](https://dataliteracy.com/nightingale-and-data-literacy/) - When it comes to data storytelling, Florence Nightingale is known for her association with one colorful polar-area diagram. But that chart was not designed to be seen in isolation. It was meant to be
- [Asking Worthwhile Questions of Your Data](https://dataliteracy.com/asking-worthwhile-questions-of-your-data/) - In order to become skilled at using data, there's one attribute we all need to develop, no matter what industry we're working in, and no matter what data tool we're working with: We need to learn how to ask worthwhile questions of our data.
- [Data Memos](https://dataliteracy.com/data-memos/) - Data designers Giorgia Lupi and Paolo Ciuccarelli reflected on their experience working with COVID-19 data, and developed 12 observations on data (and its representation) that we don’t want to forget in the “next-normal.”
- [NEW BOOK: "Read, Write, Think Data" is Now Available](https://dataliteracy.com/read-write-think-data-now-available/) - NEW BOOK: “Read, Write, Think Data” is Now Available in Paperback & eBook! Our latest book, Read, Write, Think Data, is now available for purchase in both paperback and ebook formats! You can get your copy of the paperback by ordering it on Amazon, and you can get access to the ebook on Amazon or
- [The Difference Between “Prevalence” and “Incidence” and Why We Care](https://dataliteracy.com/the-difference-between-prevalence-and-incidence-and-why-we-care/) - Estimating the risk and burden of a disease on a population is complex. Alli Torban considers how studying both incidence and prevalence can help channel much needed resources.
- [Announcing the Launch of the Data Literacy Level 1 On-Demand Course](https://dataliteracy.com/announcing-level-1-on-demand-course/) - We're excited to launch the second on-demand course of our online training academy! Data Literacy Level 1: Learning to See Data is now open for enrollment.
- [Now Available: Our Origin Story in "The Introspective Entrepreneur"](https://dataliteracy.com/now-available-our-origin-story-in-the-introspective-entrepreneur/) - In Data Literacy CEO Ben Jones's latest book, he tells the origin story of the company from his point of view, as he navigated the challenge of leaving his job at Tableau and starting a new small business from scratch.
- [3 Signs You're Working with a Dead-End Dataset](https://dataliteracy.com/3-signs-youre-working-with-a-dead-end-dataset/) - To help you identify a dead-end dataset before you spend too much time with it, we’ll share three signs to look for when you hit that download button.
- [Data Journaling: The Power and Potential of Qualitative Data Through the Lens of a Mom-to-be](https://dataliteracy.com/data-journaling-the-power-and-potential-of-qualitative-data-through-the-lens-of-a-mom-to-be/) - This data journal provides a space for people to track their pregnancy in a healthy way that doesn't cause emotional or psychological distress.
- [The Data Literacy Movement: Founded by Women, Led by Women](https://dataliteracy.com/data-literacy-women-leaders/) - Did you know? The Data Literacy Movement was FOUNDED by women and is currently being LED by women. Read about some of the talented, devoted women who breathe life into this cause day in and day out with their commitment to help others, with their contributions, and with their creativity.
- [How to Annotate Like a Designer](https://dataliteracy.com/how-to-annotate-like-a-designer/) - Do you wish the text in your charts looked more refined? I’ll show you how to make your annotations look more professionally designed with a few small tweaks using PowerPoint. Once you see these tweaks in action, you’ll begin to make these refinements more naturally just like a designer.
- [VIDEO: Avoiding Graphical Gaffes](https://dataliteracy.com/video-avoiding-graphical-gaffes/) - Our CEO Ben Jones, author of 'Avoiding Data Pitfalls' and 'Learning to See Data,' reviews 20 visualizations seen "in the wild" recently that provide opportunities to learn what to NOT to do when communicating data visually.
- [Working with Data Professionals (when you aren't one!): Post 4 - Up Leveling Your Partnership with Data Professionals](https://dataliteracy.com/working-with-data-professionals-when-you-arent-one-post-4-up-leveling-your-partnership-with-data-professionals/) - Being a people leader of a team of data professionals involves many of the same activities of people leadership for any team. However, if you are not a data professional yourself, there are some aspects that will help set you and your team up for long term success if you are attentive to them.
- [Working with Data Professionals (when you aren't one!): Post 3 - Managing a Team of Data professionals](https://dataliteracy.com/working-with-data-professionals-when-you-arent-one-post-3-managing-a-team-of-data-professionals/) - Being a people leader of a team of data professionals involves many of the same activities of people leadership for any team. However, if you are not a data professional yourself, there are some aspects that will help set you and your team up for long term success if you are attentive to them.
- [Working with Data Professionals (when you aren't one!): Post 1 - What Do Data Professionals Do?](https://dataliteracy.com/working-with-data-professionals-when-you-arent-one-post-1-what-do-data-professionals-do/) - In this blog series, Anna-Maria Steverson of Netflix will break down some critical topics including hiring data professionals, managing a team of data professionals and getting the most out of data professionals. In this first of four posts, she covers the variety of things data professionals do!
- [Working with Data Professionals (when you aren't one!): Post 2 - How to Hire a Data Professional](https://dataliteracy.com/working-with-data-professionals-when-you-arent-one-post-2-how-to-hire-a-data-professional/) - In this post, I'm going to spend some time talking about how to hire a data professional. This tends to be a bit tricky if you are not a data professional yourself.
- [Announcing the Winners of the 2021 Data Literacy Awards!](https://dataliteracy.com/2021-data-literacy-awards-winners/) - We’re thrilled to announce the winners of the 2021 Data Literacy Awards, chosen by popular vote from among the shortlist candidates nominated by the general public. These exemplary winners helped us speak the language of data even more fluently in 2021, and we’d like to congratulate all of them for being awarded this distinction.
- [Data Stories: Bringing Data to Communities](https://dataliteracy.com/data-stories-bringing-data-to-communities/) - The story of Data Stories is one about communities, equity, access, empowerment, and a changing economic landscape. As is often the case, when attempting to distill a trove of information into a single message it can be incredibly difficult to focus on a single number.
- [Kicking off our 2022 Public Course Offerings](https://dataliteracy.com/kicking-off-our-2022-public-course-offerings/) - Enrollment is now open for our first public course offering of 2022! Data Literacy Fundamentals, our most sought after foundational course and the first in our 4-part Core Course series, will now be available as a live, virtual, instructor-led public course.
- [Voting is Now OPEN for the 2021 Data Literacy Awards!](https://dataliteracy.com/vote-for-the-2021-data-literacy-awards/) - We’re delighted to introduce the 2021 Data Literacy Awards shortlist candidates, chosen from among the nominations submitted by the general public over the past month. These exemplary candidates helped us speak the language of data even more fluently in 2021, and we’d like to congratulate all of them for being chosen from among their peers.
- [NEW: The '17 Key Traits of Data Literacy' Course & Self-Assessment](https://dataliteracy.com/17-key-traits-of-data-literacy-course/) - We're excited to launch the '17 Key Traits of Data Literacy' on-demand course & self-assessment. Learn about the critical knowledge, skills, attitudes, & behaviors of data literacy, take stock of your own proficiency, and customize your learning and development path.
- [A Data Journey Through Breast Cancer](https://dataliteracy.com/a-data-journey-through-breast-cancer/) - Data Literacy COO and co-founder Becky Jones writes about her experience battling breast cancer and the role that data played in the way she and her team of doctors approached the treatment plan.
- [Nominations are OPEN for the 2021 Data Literacy Awards!](https://dataliteracy.com/nominations-are-open-for-the-2021-data-literacy-awards/) - It's that time of year again! We'd like your help identifying the individuals and content that helped to advance Data Literacy the most this past year.
- [Earn Data Literacy Training Badges](https://dataliteracy.com/training-badges-now-available/) - August 27, 2021 | We're excited to announce that learners who complete each of the four levels of our online training program will now earn a training completion badge that they can share on social media and add to their LinkedIn profile.
- [Avoiding Data Pitfalls - FREE Guide & Checklist](https://dataliteracy.com/avoiding-data-pitfalls-free-guide-checklist/) - Get this free guide and checklist to help you avoid 8 different categories of pitfalls that we all fall into from time to time.
- [How to Make an Interactive Map with No Code](https://dataliteracy.com/how-to-make-an-interactive-map-with-no-code/) - Do you want to create your own interactive maps, but you don’t know where to start? I’m here to help!
- [Take the 17 Key Traits Self-Assessment!](https://dataliteracy.com/take-the-17-key-traits-self-assessment/) - Find out your biggest data literacy strengths and development opportunities with this free online assessment tool using the 17 Key Traits of Data Literacy
- [Data Journaling: an Analog Way to Learn About Data](https://dataliteracy.com/data-journaling-an-analog-way-to-learn-about-data/) - For the last year and a half, there has been something lacking in all this new exposure to data: how to understand it. That’s why I created a tool to help folks get into data tracking, analysis and visualization — without the need for technical skills.
- [An Illustrated Guide to Data Literacy: Pie Charts](https://dataliteracy.com/an-illustrated-guide-to-data-literacy-pie-charts/) - Alli Torban kicks off her first data comic strip! Follow characters Becky & Fern as they wrestle with whether to use the controversial pie chart for the sales meeting.
- [Partnering with the Data Visualization Society](https://dataliteracy.com/partnering-with-the-data-visualization-society/) - We're proud to announce that we have partnered with DVS to give their members exclusive access to discounts on our world-class data literacy training courses and books.
- [How to Make a Beeswarm Plot in RAWGraphs and Then Edit in PowerPoint](https://dataliteracy.com/how-to-make-a-beeswarm-plot-in-rawgraphs/) - Do you want to create more complex chart types, but you feel like you don’t have the skills or the time to learn a new tool?I’m here to help! Let me show you how to make a beeswarm plot using RawGraphs and PowerPoint. We’ll go from spreadsheet to polished visualization that’s ready to impress your team in under 15 minutes!
- [Building Data Trust](https://dataliteracy.com/building-data-trust/) - Why trust in data sometimes erodes and how to prevent it from eroding in the first place. Guest blog post by Jodi Pafford, data analyst and insight sleuth from Colorado.
- [Handmade in Punjab: embroidered data in phulkaris](https://dataliteracy.com/handmade-in-punjab/) - Guest blogger Sana Ahmed reflects on the customs of Punjab women dating back to the 15th century, and how data is used in the design choices of the phulkari embroidery work to encode alignment, symmetry, enclosure, color, and more.
- [Pivot Into a Data Career: How My Journey Can Help You Start Yours](https://dataliteracy.com/pivot-into-a-data-career-how-my-journey-can-help-you-start-yours/) - Are you thinking about pivoting into a career in data? Does your first step feel daunting? I’ll share the steps you can take to confidently start your journey.
- [Happy Pi Day! Let's Settle the Score on the Most Controversial Chart Type](https://dataliteracy.com/happy-pi-day-2021/) - Where did the pie chart come from, how is it built, and, more importantly, when should it be used and when should it be avoided. Ben Jones explains in this free 15 minute instructional video from our latest course.
- [What a Data Scientist Looks Like](https://dataliteracy.com/what-a-data-scientist-looks-like/) - Data Scientist Dr. Inna Saboshchuk, PhD, Sr. Data Product Manager, describes her personal journey into her field, overcoming obstacles and preconceived notions on her way to success.
- [Anthony Starks on Recreating Du Bois’s Iconic Style](https://dataliteracy.com/anthony-starks-on-recreating-du-boiss-iconic-style/) - Allen Hillery writes about how Anthony Starks was inspired to recreate the work of W.E.B. Du Bois and how he then started an online challenge to inspire others to do the same.
- ['16 Chart Reading Tips' Checklist - Free for Subscribers](https://dataliteracy.com/16-chart-reading-tips/) - Get our free checklist to help you read and interpret the kinds of data visualizations that you come across each and every day. From the #1 New Release 'Learning to See Data' by Ben Jones
- [Be Data Lit: Creating Communities that Matter](https://dataliteracy.com/be-data-lit/) - Read the story behind the launch of the new Be Data Lit community site in the words of the site's founder, Sarah Nell-Rodriguez, and find out how you can join.
- [How W.E.B Du Bois Used Data Visualization to Debunk Social Darwinism and Tell A Story of Resilience](https://dataliteracy.com/web-du-bois-story-of-resilience/) - Celebrating the kickoff of Black History Month, 2021, read about the engaging and impactful data visualization work of American writer, sociologist, historian, and civil-rights activist, W.E.B Du Bois and his team.
- [Now Available! The 17 Key Traits of Data Literacy Ebook](https://dataliteracy.com/now-available-the-17-key-traits-of-data-literacy-ebook/) - Discover the knowledge, skills, attitudes and behaviors that highly data literate people possess and demonstrate. Take stock of your own level.
- [Announcing the Winners of the 2020 Data Literacy Awards!](https://dataliteracy.com/announcing-the-winners-of-the-2020-data-literacy-awards/) - Announcing the winners of the 2020 Data Literacy Awards, including most insightful data book, most helpful video channel, most interesting data podcast, top data community initiative, and data literacy advocate of the year
- [Jon Schwabish's "One Chart at a Time" Series](https://dataliteracy.com/jon-schwabishs-one-chart-at-a-time-series/) - Watch episode #3 of Jon Schwabish's "One Chart at a Time" series, in which Data Literacy Co-Founder shares thoughts about the Paired Bar Chart.
- [Announcing the Launch of our First On-Demand Course!](https://dataliteracy.com/announcing-the-launch-of-our-first-on-demand-course/) - Data Literacy is excited to launch the first on-demand course of their new online training academy! Data Literacy Fundamentals is now open for enrollment.
- [Data Literacy is the Key to Helping Underserved Communities](https://dataliteracy.com/data-literacy-is-the-key-to-helping-underserved-communities/) - This guest blog post by Allen Hillery talks about creating career pathways through tech apprenticeships
- [Data Literacy welcomes Allen Hillery to the Advisory Board](https://dataliteracy.com/data-literacy-welcomes-allen-hillery-to-the-advisory-board/) - Mr. Allen Hillery of Columbia University, experienced data literacy educator and talented data writer, joins the Data Literacy Advisory Board.
- [Announcing the Winners of the 2019 Data Literacy Awards](https://dataliteracy.com/announcing-the-winners-of-the-2019-data-literacy-awards/) - See who won for the best data book, podcast, video channel, community initiative and advocate of the year!
- [Score a Free Webinar for your Book Club!](https://dataliteracy.com/bulk-book-order-offer/) - Place a bulk order of Avoiding Data Pitfalls for your team or goup between now and the end of Jan, 2020, and book a free virtual session with the author!
- [Tableau Training for Journalists at USC Annenberg](https://dataliteracy.com/tableau-training-for-journalists-at-usc-annenberg/) - See the 40 steps involved with turning two data files about disease count and county population into an interactive dashboard that allows readers to see rates over time
- [Announcing the Winners of the 1st Data Literacy Immigrant Spirit Scholarship Contest!](https://dataliteracy.com/announcing-the-winners-of-the-1st-data-literacy-immigrant-spirit-scholarship-contest/) - Please join us in congratulating the following 3 individuals who have won the 1st ever Data Literacy "Immigrant Spirit" Scholarship Giveaway! They win a full pass to any upcoming Data Literacy training course, a $500 value! Mahrad Mohammadi was born in Iran, now lives in the United States, and he is the Acting Ground Safety
- [Announcing 3 Course Scholarships for Immigrants!](https://dataliteracy.com/announcing-3-course-scholarships-for-immigrants/) - Three immigrants who enter will be chosen at random on October 18, 2019!
- [Announcing the Data Literacy Fall 2019 Line Up!](https://dataliteracy.com/announcing-the-data-literacy-fall-2019-line-up/) - Registration is now open for two new online courses in data literacy. 'How to Read and Interpret Data Visualizations' and 'How to Explore and Communicate Data'. Sign up now!
- [Announcing 2 New Online Data Literacy Courses!](https://dataliteracy.com/announcing-2-new-online-data-literacy-courses/) - Registration is now open for two new online courses in data literacy. 'How to Read and Interpret Data Visualizations' and 'How to Explore and Communicate Data'. Sign up now!
- [Whiteboard Series #001: Four Data Scale Types](https://dataliteracy.com/whiteboard-series-001-four-data-scale-types/) - Consider different ways collecting data about a bottle of wine in order to learn all about Nominal, Ordinal, Interval & Ratio measurement scales
- [Website Launch Celebration a Success!](https://dataliteracy.com/website-launch-celebration-a-success/) - A recap of the festive happy hour, and all the information and links to the talented designers who helped make DataLiteracy.com a reality!
- [Free Data Practices Courseware Now Available](https://dataliteracy.com/free-data-practices-courseware-now-available/) - The team at data.world has published free and open course materials designed to help everyone from the novice to the expert data practitioner
- [Embarking on a New Adventure!](https://dataliteracy.com/embarking-on-a-new-adventure/) - Data Literacy, LLC founder Ben Jones talks about why he decided to leave Tableau Software to start up a company focused on teaching the language of data
- [Hello, World! Announcing the Launch of DataLiteracy.com](https://dataliteracy.com/hello-world-announcing-the-launch-of-dataliteracy-com/) - The unfounded myths surrounding what constitutes evidence in sexual assault and sexual harassment cases should not deter victims from pursuing justice in a court of law.

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- [AI Literacy Fundamentals](https://dataliteracy.com/ai-literacy-fundamentals/) - This course will lay a firm foundation in the core principles of AI, its history and evolution, concepts of machine learning and deep learning, everyday applications, and ethical considerations.
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- [Working with Data Professionals](https://dataliteracy.com/working-with-data-professionals/) - This course will teach you all about OpenAI's new AI chatbot, ChatGPT. You'll learn how to get started using it, you'll learn about its history and emergence, and you'll learn about important warnings and caveats associated with using it.
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- [Advancing Responsible AI](https://dataliteracy.com/advancing-responsible-ai/) - Enhance your understanding of AI’s ethical implications with this on-demand program lead by head instructor & Data Literacy CEO Ben Jones.
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- [Data Literacy Level 1 v2.0](https://dataliteracy.com/data-literacy-level-1/) - Build your confidence in the basic concepts of Data Literacy in this on-demand program with head instructor & Data Literacy CEO Ben Jones and Senior Data Literacy Advocate Alli Torban.
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- [Chantilly Jaggernauth](https://dataliteracy.com/about-us/chantilly-jaggernauth/) - Chantilly Jaggernauth is the founder of the data training program Millennials in Data, a Tableau Zen Master, and a Data Literacy advocate.
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- [Janet Wilson](https://dataliteracy.com/about-us/janet-wilson/) - Janet is a collaborative course builder who knows how to take complex subject matter and work with a team to create experiences that meets & exceeds the needs of learners.
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- [Alberto Cairo](https://dataliteracy.com/about-us/alberto-cairo/) - Read about the background and expertise of Data Literacy Advisory Board Member Alberto Cairo
- [Giorgia Lupi](https://dataliteracy.com/about-us/giorgia-lupi/) - World famous infographics designer and data artist with original work on display at MoMA. Author of best-selling data humanism books Dear Data & Observe, Collect, Draw.
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- [Allen Hillery](https://dataliteracy.com/about-us/allen-hillery/) - Allen is very passionate about data literacy and curates an article series that focuses on the importance of creating data narratives and spotlighting notable figures on how their use of storytelling made major impacts on society.
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- [SuperDataScience](https://dataliteracy.com/superdatascience/) - SuperDataScience Episode #726 On October 23, 2023, Data Literacy CEO and the author of ‘Leading in the Age of Data,’ Ben Jones sat down with SuperDataScience podcast host Jon Krohn to have a discussion about what it takes to be a great leader in the age of data. WATCH THE SHOW NEW BOOK: Leading in
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## Products

- [Data Storytelling for IMPACT Public Workshop](https://dataliteracy.com/product/data-storytelling-for-impact-public-workshop/) - Data Storytelling for IMPACT is taught by Alli Torban, Sr. Data Literacy Advocate at Data Literacy and author of Chart Spark. This course is a live, instructor-led workshop open to the general public. When: October 27th – 30th, 2025 (see schedule below)Where: Online (Zoom)Price: $495 per personGroup Size: Only 10 seats (small on purpose, so you get real feedback) What’s included? Access to the full on-demand IMPACT course ($145 value) A re-usable Data Storytelling for IMPACT Workbook (fillable PDF format) A 30-minute 1:1 coaching session with Alli ($100 value, redeem any time!) A digital comic book on data storytelling concepts ($15 value) Workshop recordings so you can catch up if needed Personalized feedback on your data story as you build it 📆 Workshop Schedule Monday, Oct. 27🕐 1–3 PM ETLive Workshop: I.M.P. – Insight, Message, Plan (story structure) Tuesday, Oct. 28🕐 1–2 PM ETOffice Hours (optional): Project Q&A + Personalized Coaching Wednesday, Oct. 29🕐 1–3 PM ETLive Workshop: A.C.T. – Align, Clarify, Test (visuals + feedback) Thursday, Oct. 30🕐 1–1:30 PM ETShowcase: Optional attendee presentations Tools Used: Access the modules on a Mac or PC laptop. No familiarity with specific data analytics software tools necessary.
- [All-Access Subscription](https://dataliteracy.com/product/all-access-subscription/) - Individual, All-Access annual subscription, payable in monthly or annual installments. This plan grants you full access to our entire expanding library of data & AI literacy courses, along with their accompanying course books and educational materials. Subscribe today and invest in your data & AI literacy learning journey. Choose monthly or annual payments with a 12-month minimum commitment. For monthly subscriptions, cancel any time after the first year. Subscription auto-renews unless canceled before your renewal date. No refunds for early cancellation.
- [Working with Data Professionals (even if you aren't one!) On-Demand Course](https://dataliteracy.com/product/working-with-data-professionals-on-demand-course/) - 🚨Buy this course on its own, or click here for our All-Access Subscription options and unlock this course plus our entire course library! Whether your background is in the data field or something completely unrelated, it can be challenging to know how to set up your organization’s data efforts for success. Much of that success starts with the people you have working on these efforts. Hiring the right data professionals for the right data job is step one. Then it’s a matter of onboarding them, managing them, and assessing their performance. For those not directly responsible for the team, how to successfully partner with these professionals to achieve your organizations data goals is of critical importance as well. In this course, you will learn how to effectively hire, manage, and optimize your working relationship with data professionals to leverage the talents of data professionals and drive impactful organizational change. By the end you'll have learned: What Do Data Professionals Do?: A brief overview of different types of data work and the data skills needed to perform them. How to Hire a Data Professional: Advice on how to hire someone who has the data skills needed to do the types of data work your organization needs. Managing a Team of Data Professionals: High-level methods and approaches for leading a team of data professionals. Measuring the Performance of Data Professionals: How to track whether or not your data professionals are performing well. Up-Leveling Your Partnership with Data Professionals: Best practices for getting the most out of your partnership with the data professionals in your organization. This course was authored by industry veteran Anna-Maria Steverson. Interested in team pricing? Inquire here.
- [Data Literacy for Leaders On-Demand Course](https://dataliteracy.com/product/data-literacy-for-leaders/) - Learn how to become an effective Leader in the Age of Data. This on-demand course focuses on 7 core aspects of data leadership: ethics, purpose, data, technology, people, process, and culture.
- [Data Literacy Fundamentals On-Demand Course v2.0](https://dataliteracy.com/product/data-literacy-fundamentals-v2-on-demand-course/) - 🚨Buy this course on its own, or click here for our All-Access Subscription options and unlock this course plus our entire course library! Learn the language of data with Data Literacy Fundamentals: Understanding the Power & Value of Data. In this course you will lay a firm foundation in Data Literacy by putting in place 8 building blocks of understanding that will help you grasp what data is, how it’s used, and ways to think about incorporating it into different areas of your life and career. Data literacy is essential in today's world, vital for every role and department. Take control and boost your confidence in working with data. No prior experience or skills required. This course was built with all experience levels in mind. Data Literacy Fundamentals was developed by Ben Jones, Data Literacy CEO and Professor of Data Visualization Theory at The University of Washington’s Foster School of Business. By the end you’ll have learned: The 1 Overall Goal of Data The 2 Systems of human thinking involved The 3 Areas of life where data matters The 4 Different data scale types The 5 Forms of data analysis The 6 Ways of displaying data The 7 Groups of data activities teams carry out The 8 Questions to ask your data upfront Interested in team pricing? Inquire here. And don’t forget! You can always preview the first lesson for free.
- [Chart Spark On-Demand Course](https://dataliteracy.com/product/chart-spark/) - 🚨Buy this course on its own, or click here for our All-Access Subscription options and unlock this course plus our entire course library! Learn how to think more creatively so you can communicate data in innovative, impactful ways. Led by the acclaimed information designer and "Data Viz Today" podcast host, Alli Torban, this course offers a unique framework for weaving creativity into your data viz projects. Alli understands the struggles faced by data professionals who don't identify as "creative types." She shares her journey from data analyst to creative information designer and distills years of experience and interviews with successful data visualization designers. With 10 innovative prompts and 60 minutes of engaging video content, Alli brings the concepts of her transformative guide to life, demonstrating practical techniques to ignite your creative thinking. You’ll learn how to nurture your creativity and spark those moments of inspiration when you need them most. Tailored for data professionals seeking to elevate their creations, this course is your blueprint for integrating impactful creativity into every project. Interested in team pricing? Inquire here.
- [AI Literacy Fundamentals On-Demand Course](https://dataliteracy.com/product/ai-literacy-fundamentals-on-demand-course/) - 🚨Buy this course on its own, or click here for our All-Access Subscription options and unlock this course plus our entire course library! This course is for anyone who wants to join the AI conversation happening in their workplace, in their community, or online. This course will empower you to lay a firm foundation in the core principles of AI, its history and evolution, concepts of machine learning and deep learning, everyday applications, and ethical considerations, enabling informed discourse on AI's societal impact and future directions. AI Literacy Fundamentals was developed by Ben Jones, Data Literacy CEO and Professor of Data Visualization Theory at The University of Washington’s Foster School of Business. By the end of this course, you will be able to: Understand the foundational concepts and definitions of AI List the key figures and historical developments of the field of AI Distinguish between different types of machine learning Understand the basic concepts behind deep learning Critically evaluate AI applications in various domains, and Debunk common myths, fostering a balanced perspective on AI's potential benefits and ethical implications.
- [Harnessing Generative AI On-Demand Course](https://dataliteracy.com/product/harnessing-generative-ai-on-demand-course/) - 🚨Buy this course on its own, or click here for our All-Access Subscription options and unlock this course plus our entire course library! This course equips you with a comprehensive understanding of generative AI, from its foundational technologies and practical applications to crafting effective prompts and strategic industry use. Dive deep into the ethical considerations, addressing biases and privacy concerns, and learn to implement safeguards to ensure fair and responsible AI deployment. Harnessing Generative AI was developed by Ben Jones, Data Literacy CEO and Professor of Data Visualization Theory at The University of Washington’s Foster School of Business. This on-demand, self-paced course consists of six lessons and is designed to be completed in approximately three hours. Progress through the six lessons at your own pace: What is Generative AI? Learn what generative AI is including examples of current technologies. The Technology Behind Generative AI: Understand generative AI's underlying technologies, such as neural networks and transformers. Effective Prompting of Generative AI: Master effective prompting strategies to craft, test, and refine prompts, enhancing the accuracy and relevance of AI-generated content through strategic adjustments and feedback. Strategic Applications of Generative AI: Explore generative AI tools and techniques in real-world contexts, such as content creation and data analysis. Avoiding Generative AI Pitfalls: Discover strategies to mitigate risks and enhance the safety and fairness of generative AI implementations. Ethical Considerations of Generative AI: Identify and evaluate ethical concerns and biases in AI applications, including potential impacts on privacy and data security. Interested in team pricing? Inquire here.
- [Data Literacy Level 1 On-Demand Course v2.0](https://dataliteracy.com/product/data-literacy-level-1-v2-on-demand-course/) - 🚨Buy this course on its own, or click here for our All-Access Subscription options and unlock this course plus our entire course library! Data Literacy Level 1: Learning to See Data is crafted to equip you with a fundamental understanding of how to interpret a variety of chart types and their nuances. Throughout the course's 8 lessons, you'll learn how the human brain processes visual data and delve into essential chart types such as bar charts, pie charts, histograms, scatter plots, and more. Each lesson thoroughly explores the building blocks, effective use cases, common pitfalls, and possible variations of each chart type, empowering you to analyze data visualizations confidently and make well-informed decisions. Additional Options: Team Pricing: Tailored plans are available for teams and organizations. Inquire here for more information. Start with Fundamentals: For those new to the field, pairing this course with Data Literacy Fundamentals is recommended for a comprehensive foundation in data literacy.
- [Advancing Responsible AI On-Demand Course](https://dataliteracy.com/product/advancing-responsible-ai-on-demand-course/) - 🚨Buy this course on its own, or click here for our All-Access Subscription options and unlock this course plus our entire course library! Advancing Responsible AI is designed to help you understand and implement ethical AI practices across various professional fields. Through six comprehensive lessons, this course offers deep insights into building fairer, more transparent systems and provides practical strategies to align with key governance frameworks and ethical standards. You'll gain essential skills to assess, apply, and advocate for responsible AI usage, making it invaluable for AI developers, data scientists, technology policy experts, and business leaders involved with AI systems. Each lesson is divided into three focused sections: Core Concepts, Real-World Examples, and Practical Applications, facilitating a structured and effective learning experience. Additional Options: Team Pricing: Tailored plans are available for teams and organizations. Inquire here for more information. Explore Other Courses in Our AI Literacy Series: Consider pairing this course with AI Literacy Fundamentals or Harnessing Generative AI.
- [21 Key Traits of Data & AI Literacy](https://dataliteracy.com/product/21-key-traits-of-data-and-ai-literacy/) - What are some of the key traits of Knowledge, Skills, Attitudes, and Behaviors that people who are highly literate in data and AI possess? In this course, you'll learn about 21 key traits, and you'll assess your own proficiency in each of these traits.
- [Data Literacy Level 2 On-Demand Course v2.0](https://dataliteracy.com/product/data-literacy-level-2-on-demand-course-v2/) - 🚨Buy this course on its own, or click here for our All-Access Subscription options and unlock this course plus our entire course library! Data Literacy Level 2: The WISDOM Framework for Data Analysis is built to equip you with a tool-agnostic approach to exploring data, answering worthwhile questions, and communicating your insights to an audience. Additional Options: Team Pricing: Tailored plans are available for teams and organizations. Inquire here for more information. Start with Fundamentals: For those new to the field, pairing this course with Data Literacy Fundamentals is recommended for a comprehensive foundation in data literacy.
- [Data Storytelling for IMPACT On-Demand Course](https://dataliteracy.com/product/data-storytelling-for-impact-on-demand-course/) - 🚨Buy this course on its own, or click here for our All-Access Subscription options and unlock this course plus our entire course library! Data Storytelling for IMPACT is built to equip you with an agile-inspired approach to creating clear and compelling data stories for your audience that drives the decisions and changes that need to be made. Additional Options: Team Pricing: Tailored plans are available for teams and organizations. Inquire here for more information. Start with Fundamentals: For those looking to boost their creativity in data visualization and storytelling, pairing this course with the Chart Spark course, also by Alli, is recommended.
- [Group Training for York Region](https://dataliteracy.com/product/york-group/) - Select the courses you'd like to include in your training group, give your group a name, and become a group leader, giving you the ability to add users and view course progress. If you'd like to take the courses yourself, be sure to the check the "Enroll Me" box below.
- [Data Literacy Fundamentals for York](https://dataliteracy.com/product/data-literacy-fundamentals-york/) - Learn the language of data with Data Literacy Fundamentals: Understanding the Power & Value of Data. In this course you will lay a firm foundation in Data Literacy by putting in place 8 building blocks of understanding that will help you grasp what data is, how it’s used, and ways to think about incorporating it into different areas of your life and career. Data literacy is essential in today's world, vital for every role and department. Take control and boost your confidence in working with data. No prior experience or skills required. This course was built with all experience levels in mind. Data Literacy Fundamentals was developed by Ben Jones, Data Literacy CEO and Professor of Data Visualization Theory at The University of Washington’s Foster School of Business. By the end you’ll have learned: The 1 Overall Goal of Data The 2 Systems of human thinking involved The 3 Areas of life where data matters The 4 Different data scale types The 5 Forms of data analysis The 6 Ways of displaying data The 7 Groups of data activities teams carry out The 8 Questions to ask your data upfront Interested in team pricing? Inquire here. And don’t forget! You can always preview the first lesson for free.
- [Data Literacy Fundamentals 2nd edition eBook (PDF)](https://dataliteracy.com/product/data-literacy-fundamentals-2nd-ed-ebook/) - Purchase an interactive PDF copy of the 2nd edition of 'Data Literacy Fundamentals' by Ben Jones (Data Literacy Press, 2025). Lay a firm foundation for yourself by developing a keen understanding of the power and value of data.
- [How to Explore a Dataset with Generative AI](https://dataliteracy.com/product/how-to-explore-a-dataset-with-generative-ai/) - By the end of this 90 minute workshop, you’ll know how to use generative AI to explore a dataset, uncover trends, patterns, and insights, and identify and correct mistakes for better decision-making. Set-Up: Learn a few generative AI tool options and how to prepare a dataset for exploration. Explore and Analyze: Use specific prompts to uncover trends, patterns, and insights while learning how to spot and investigate potential AI-generated mistakes in data analysis. Apply to Your Work: Discover ways generative AI is being used for better decision-making and how to integrate it effectively into your workflow. When: December 12th from 12-1:30 pm EST Where: Online via Zoom.us Instructor: Ben Jones, CEO & Co-Founder of Data Literacy
- [AI Literacy Fundamentals: Helping You Join the AI Conversation ebook (PDF)](https://dataliteracy.com/product/ai-literacy-fundamentals-ebook-pdf/) - The companion book to our on-demand course AI Literacy Fundamentals: Helping You Join the AI Conversation. Feeling overwhelmed by AI? It's not you—it's the breakneck speed of technological progress. To quickly get into the AI conversation, you need a clear and simple foundation of knowledge to build on. This book is a friendly primer on the basic concepts of AI, how it's already snuck into our daily lives, and what we need to know to prepare for the future. Ben Jones, an expert at breaking down technical concepts from teaching thousands of people the basics of data literacy, lays out everything you need to know to join the AI conversation, from the history of AI to the deep learning revolution happening today. This technology is here to stay. Time for you to pull a seat up to the table. Praise for 'AI Literacy Fundamentals' "I can't think of a better written and more thoroughly researched introduction to the fundamental concepts of AI Literacy than Ben's wonderful book. I cannot recommend it enough. Read. Be inspired. Be ready for our changing world." -James Wilson, author of Artificial Negligence
- [Data Citizen Objective Data Literacy Assessment](https://dataliteracy.com/product/data-citizen-assessment/) - A skills-based objective data literacy assessment that tests an individuals knowledge of the foundational aspects of data literacy.
- [Visual Interpreter Objective Data Literacy Assessment](https://dataliteracy.com/product/visual-interpreter-assessment/) - A skills-based objective data literacy assessment that tests an individual's knowledge of the most common chart and graph types and their pitfalls.
- [Data Explorer Objective Data Literacy Assessment](https://dataliteracy.com/product/data-explorer-assessment/) - A skills-based objective data literacy assessment.
- [Chart Spark: Harness your creativity in data communication to stand out and innovate ebook (PDF)](https://dataliteracy.com/product/chart-spark-ebook-pdf/) - The companion book to the Chart Spark course COMING SOON! Sign up for the waitlist! Do you want to be more creative in your data communication? Looking for specific creativity advice tailored for those in the data field? Chart Spark is the transformative guide you've been waiting for. As an acclaimed information designer and host of the popular podcast Data Viz Today, Alli Torban understands the struggles faced by data professionals who don't identify as "creative types." She shares her journey from data analyst to creative information designer and distills years of experience and interviews with successful data visualization designers into this concise, actionable book. Who's It For? If you work with data or data visualization and want to communicate with more impact, this book is for you. It's not another "inspiration book" filled with glossy infographics; instead, it's a practical guide designed to change your perspective on creativity and integrate it into your work. Each chapter includes actionable prompts designed to trigger your creativity, rather than just admire the finished product of others. Table of Contents: Introduction: What is creativity and why should you care? Section I: CARE Chapter 1: Expand your mental boundaries with the 'Bad Gifts' prompt Chapter 2: Cultivate your inspiration with the 'X-RAY' prompt Chapter 3: Build your habits with the 'Recess List' prompt Section II: COAX Chapter 4: Blast through project paralysis with the 'Idea Isosceles' prompt Chapter 5: Immediately see through a new lens with the 'Break-the-Box' prompt Chapter 6: Find stories like an editor with the 'CTR' prompt Section III: COMMUNICATE Chapter 7: Find an appropriate balance with the '4Q' prompt Chapter 8: Explain it with a visual metaphor with the 'Haystack' prompt Chapter 9: Mix different mediums and experiences with the 'Tango' prompt Conclusion: What should you do next? Also included: a complimentary digital Spark Journal packed with all the book's actionable prompts, enabling you to dive right into your creative process. Harness your creativity and transform your data communication with Chart Spark! Praise for Chart Spark: "A perfect starting point for dataviz practitioners who might be intimidated by the creative process. This friendly book shows that creativity is a practice, not an innate talent!" —Stefanie Posavec, designer, artist, and co-author of Dear Data "Alli’s not here to mint new Picassos, but to help the reader stretch and grow. If you follow her guidance and prompts, it will change the way you think about creativity." —Steve Wexler, Co-Author of The Big Book of Dashboards "In Chart Spark, Alli celebrates the ways creativity happens off a computer with prompts and brainstorming structures that can help anyone plot a path towards more meaningful data visualizations." —Amanda Makulec, Executive Director of the Data Visualization Society "This book is neatly constructed into logical chapters that help everyone, regardless of background, grasp the basics and then elevate their creativity step-by-step." —Andy Kirk, Independent Data Visualisation Expert
- [ChatGPT Basics eBook (PDF)](https://dataliteracy.com/product/chatgpt-basics-ebook-pdf/) - ChatGPT Basics: Exploring Its Origins, Uses, and Misuses by Ben Jones The companion book to our on-demand course ChatGPT Basics: Foundations & Getting Started. ChatGPT Basics is the ultimate beginner's guide to understanding and utilizing the groundbreaking AI chatbot, ChatGPT, developed by OpenAI. This comprehensive book provides a thorough yet accessible introduction to the world of ChatGPT, enabling you to harness its potential effectively and responsibly. Starting with an engaging account of ChatGPT's inception and the impact it has had on the tech world, the book delves into the underlying technology that powers ChatGPT, offering insights into its inner workings. As you progress, you'll be introduced to the ethical and practical concerns surrounding this innovative tool, preparing you to use it cautiously and avoid potential pitfalls. With step-by-step guidance, you'll learn how to set up your account, explore various platforms to interact with ChatGPT, and create effective prompts that elicit meaningful responses. The book also offers recommendations on using ChatGPT across multiple interfaces, including the OpenAI website, Microsoft's "new Bing" search engine, the ChatGPT Basics on-demand course, and even unofficial mobile applications for iOS and Android devices. ChatGPT Basics is an essential resource for anyone eager to learn about ChatGPT and its applications. By the end of the book, you'll be equipped with the knowledge and skills necessary to employ ChatGPT responsibly, maximizing its potential while minimizing potential harm. So, get ready to embark on your ChatGPT journey and dive into the fascinating world of AI chatbots!
- [Leading in the Age of Data: Your Guide to the 7 Factors of Team Empowerment ebook (PDF)](https://dataliteracy.com/product/leading-in-the-age-of-data-ebook-pdf/) - The companion book to our on-demand course Data Literacy for Leaders: Empowering Organizations in a Data-Rich World. In "Leading in the Age of Data: Your Guide to the 7 Factors of Team Empowerment," Ben Jones, Co-Founder and CEO of Data Literacy, reveals that effective leadership in today's digital era goes beyond data knowledge - it requires fostering an environment where data is used intelligently, ethically, and innovatively. Built on the foundation of the Data Literacy Score Team-Based Assessment tool, this pioneering work explores the 7 crucial factors for successful data leadership: Ethics, Purpose, Data, Technology, People, Process, and Culture. Jones lays bare the truth that the proficiency in data utilization is not just about the volume of data or the technological advancements at your disposal; instead, it is a balanced approach across these seven factors that breeds success. Jones meticulously breaks down each factor into 7 guiding principles, providing a total of 49 actionable strategies that leaders can implement directly. These principles are not just theoretical; they come equipped with an opportunity for self-assessment, allowing readers to identify their strengths and areas needing improvement. By the end of the book, you will have your own seven-pointed Data Leadership Compass to guide you through the intricate journey of data leadership. Not just for IT executives, this insightful guide equips leaders of all industries and disciplines with the tools, techniques, and philosophies necessary to uplift their team's data literacy. It invites you to transcend the traditional realms of leadership and enter an era where data informs decision-making, innovation, and growth. Join Ben Jones on a transformative journey to harness the full potential of data and evolve into a leader who embraces the complexities, the challenges, and the opportunities of a data-rich world. The data era is here; it's time to lead it. Praise for 'Leading in the Age of Data' "For decades, we've focused on technology and data for the answers. Yet they were never there. The answers have always been with... people. Today, our work lives and personal lives are changing so rapidly that we struggle to maintain a sense of focus, purpose and belonging. Those leaders who can harness the power of data to innovate, augment and enhance the customer and employee experience, will be the new pioneers of modern talent environments. In Leading in the Age of Data, Ben carefully unpacks the system of seven levers required to understand and create the conditions for raising awareness, wiring new connections and creating new habits by harnessing data. A must read for anyone serious about modern leadership in the age of data!" -Valerie Logan, CEO of The Data Lodge "Ben Jones's 'Leading in the Age of Data' is a must-read for all leaders. It's not just about DATA but about understanding people, ethics, and culture. Ben breaks down complex topics into clear lessons, showing how true leadership balances heart and numbers. My favorite section was the one on PEOPLE - 'in order to realize the benefits of technology, people almost always need to obtain some working knowledge and skills first.' While technology evolves, it's the human factor—people with their knowledge, ethics, and culture—that remains the most pivotal element in ensuring success. I recommend this book to anyone that wants to be a leader in the age of data!" -Kate Strachnyi, Founder of DATAcated "Organizations, vendors, and employees all say "Hey, you need a data culture." But there are very few practical guides that actually provide the necessary steps. This is the vital gap the book fills. Ben's conversational writing style is always a pleasure to read. -Andy Cotgreave, Co-author of The Big Book of Dashboards
- [Working with Data Professionals (even if you aren't one!) ebook (PDF)](https://dataliteracy.com/product/working-with-data-professionals-ebook-pdf/) - Working with Data Professionals (even if you aren't one!) by Anna-Maria Steverson The companion book to our on-demand course Working with Data Professionals (even if you aren't one!). In today's increasingly data-rich world, understanding the nature of data work and effectively collaborating with data professionals has become a critical skill. Whether you're a manager, team leader, or a business decision-maker, knowing how to leverage the talents of data professionals can be your organization's game-changer. "Working with Data Professionals (even if you aren't one!)" is a helpful guide to enhancing your understanding of the data professional's world. Authored by industry veteran Anna-Maria Steverson, this book is a practical, hands-on roadmap to optimizing your organization's data capabilities. This book will delve into the depth and breadth of data work, including: Understanding what data professionals do and the unique skill sets they possess. Navigating the process of hiring and managing a data team. Learning how to effectively measure the performance of data professionals. Building robust, collaborative relationships that enhance both data quality and usage in your organization. But this book offers more than just knowledge; it provides you with the tools and insights needed to transform this knowledge into effective action. By the end, you'll be equipped to cultivate a successful data team, drive your organization's data strategy, and maximize the value of your data assets. "Working with Data Professionals" by Anna-Maria Steverson is more than a book - it's your guide to navigating the future of business. If you're ready to unlock the power of data in your organization and accelerate your career, this book is your key. Step into the world of data with confidence and strategic insight. Your journey starts here.
- ['Learning to See Data eBook' (PDF)](https://dataliteracy.com/product/learning-to-see-data-ebook-pdf/) - Purchase the PDF copy of 'Learning to See Data' by Ben Jones (Data Literacy Press, 2020). Learn how to interpret the visual language of charts and maps.
- [Read, Write, Think Data eBook (PDF)](https://dataliteracy.com/product/read-write-think-data-ebook-pdf/) - Read, Write, Think Data: A Step-by-Step Guide to Turning Data Into Wisdom by Ben Jones The companion book to our on-demand course Data Literacy Level 2: Working Effectively with Data. Reading and interpreting charts and dashboards has become a daily task for all of us, but these days many of us find ourselves needing to work with raw data more and more often. The spreadsheets and databases we come across don’t announce their insights to us upfront. Instead, they require us to roll up our sleeves and sift them to find what’s hidden and valuable within them, like gold in a mine. Read, Write, Think Data by Ben Jones presents a tool-agnostic framework for posing powerful questions of data, preparing it for analysis, exploring what’s there, arriving at the “Aha!” moment, and then turning that insight into a compelling message that will help you drive change within your organization. The process can be followed with any tool, and multiple “how to” tutorials are included within to help you learn the ropes. “Want to learn how to read and use charts? That shouldn’t take long. How about creating charts? There are some great tools available that have short learning curves. But extracting, understanding, distilling, and accurately and ethically communicating insights from data? That’s not a trivial undertaking, but with this book, Ben will get you and your organization there a lot faster.” -Steve Wexler, Author of The Big Picture and Co-Author of The Big Book of Dashboard

## Courses

- [Working with Data Professionals (even if you aren't one!)](https://dataliteracy.com/courses/working-with-data-professionals/)
- [Data Literacy for Leaders](https://dataliteracy.com/courses/data-literacy-for-leaders/)
- [Data Literacy Fundamentals v2.0](https://dataliteracy.com/courses/data-literacy-fundamentals-v2/) - Welcome to Data Literacy Fundamentals! We are in the Fourth Industrial Revolution and data has emerged as its most prized asset. Fluency in the language of data is now a non-negotiable skill for those seeking high paying jobs, and for those looking to use data to solve problems at a global or local level. Data
- [Chart Spark: Innovative Thinking in Data Communication](https://dataliteracy.com/courses/chart-spark/) - Course Structure The course consists of five sections: Introduction Coax Care Communicate Conclusion Within each section, there are subtopics that include a creative prompt for you to try out. Use your own notebook for the prompts, or download your copy of the Spark Journal in the Introduction and have it handy during each lesson. We
- [AI Literacy Fundamentals](https://dataliteracy.com/courses/ai-literacy-fundamentals/) - AI literacy is the ability to recognize, grasp, engage with, and critically assess artificial intelligence technologies and their impacts. This course is for anyone who wants to join the AI conversation happening in their workplace, in their community, or online. This course will empower you to lay a firm foundation in the core principles of
- [Data Literacy Level 1 v2.0](https://dataliteracy.com/courses/data-literacy-level-1-v2/) - This course is for anyone who regularly reads, interprets, or designs data visuals—charts, maps, and dashboards. Many of us encounter such data visualizations at work and at home, and few of us received the formal training to make sense of them. This course will turn you into a fluent chart reader by teaching you the
- [Harnessing Generative AI](https://dataliteracy.com/courses/harnessing-generative-ai/) - This course is designed for professionals, students, and enthusiasts seeking to effectively utilize generative AI and navigate its rapidly evolving technological landscape. This course will guide you through the fundamentals, practical applications, and ethical considerations of generative AI. You'll learn effective prompting techniques, explore strategic implementations, and develop critical thinking skills to assess the impact
- [Advancing Responsible AI](https://dataliteracy.com/courses/advancing-responsible-ai/) - Advancing Responsible AI equips you with the tools and knowledge to navigate the ethical challenges of AI development. Through six comprehensive lessons, you'll explore key principles like fairness, transparency, privacy, and accountability, while learning how to minimize AI's societal and environmental impact. This course empowers you to drive responsible innovation, ensuring AI technologies are used
- [21 Key Traits of Data & AI Literacy](https://dataliteracy.com/courses/21-key-traits-of-data-ai-literacy/) - Welcome to the 21 Key Traits of Data & AI Literacy Course! Growing in data & AI literacy is one of the most important and strategic development paths we can embark upon. On a daily basis, our world presents us with data in many forms – numerical or graphical, raw or refined, static or dynamic.
- [Data Storytelling for IMPACT](https://dataliteracy.com/courses/data-storytelling-for-impact/) - Welcome to the Data Storytelling for IMPACT! Data Storytelling for IMPACT teaches you how to turn data into clear stories that drive decisions and action. This course is designed for professionals who want to present their analysis in a way that’s clear, creative, and confidently delivered. You’ll learn the IMPACT Framework, which is an iterative,
- [Data Literacy Level 2 v2.0](https://dataliteracy.com/courses/data-literacy-level-2-v2/) - Welcome to the Data Literacy Level 2 Course: The WISDOM Framework for Data Analysis! Data Literacy Level 2 teaches you how to transform raw data into actionable insights using the WISDOM Framework for Data Analysis. This course is for professionals who want to move beyond simply reading charts to independently analyzing data and making data-driven
- [Data Literacy Level 2](https://dataliteracy.com/courses/data-literacy-level-2/)
- [Welcome to Group Training for York](https://dataliteracy.com/courses/welcome-to-group-training-for-york/) - Welcome to Data Literacy training for York Region members! We're excited to kick off this group training session with you. Here are some instructions to help you get started: Visit your My Account page...
- [Data & AI Literacy Pre-Test for 3Degrees](https://dataliteracy.com/courses/data-ai-literacy-pre-test-for-3degrees/) - Data & AI Literacy Pre-Test for 3Degrees Welcome to this 30 question objective assessment of your current data and AI literacy knowledge! The results of this test will be used to inform upcoming training efforts at 3Degrees, and will not be used in any way to assess or rate your job performance.
- [Data Literacy Fundamentals Post-Course Knowledge Check](https://dataliteracy.com/courses/data-literacy-fundamentals-final-quiz/) - Congratulations on completing the Data Literacy Fundamentals Course: Understanding the Power & Value of Data! Let's perform a quick check of your understanding of some of the main concepts covered in the course. There are 10 questions in this quiz, and you'll have 15 minutes to complete it. Read each question carefully and choose one
- [Data Literacy Placement Assessment](https://dataliteracy.com/courses/data-literacy-placement-assessment/) - Welcome to the Data Literacy Placement Assessment! The purpose of this brief assessment is to figure out which of the Data Literacy courses are well suited for you based on your current level of knowledge of the topics covered in the different courses. There are two parts to the assessment. Each part has 10 multiple
- [Data Literacy Level 1 Post-Course Knowledge Check](https://dataliteracy.com/courses/data-literacy-level-1-final-quiz/) - Congratulations on completing the Data Literacy Level 1 Course: Learning to See Data! Let's perform a quick check of your understanding of some of the main concepts covered in the course. There are 10 questions in this quiz, and you'll have 15 minutes to complete it. Read each question carefully and choose one or more
- [Data Literacy Level 1 Pre-Course Knowledge Check](https://dataliteracy.com/courses/data-literacy-level-1-pre-course-knowledge-check/) - Welcome to the Data Literacy Level 1 Pre-Course Knowledge Check! The purpose of this knowledge check is to assess your understanding of critical course concepts before going through the sessions themselves. Our level 1 course helps you become more familiar with the visual language of data. The knowledge check has 10 multiple choice questions and
- [Data Literacy Fundamentals](https://dataliteracy.com/courses/data-literacy-fundamentals/)
- [Data Literacy Level 1](https://dataliteracy.com/courses/data-literacy-level-1/)
- [Data Literacy Placement Assessment - NBB](https://dataliteracy.com/courses/placement-assessment-nbb/) - Welcome to the Data Literacy Placement Assessment! The purpose of this brief assessment is to figure out which of the Data Literacy courses are well suited for you based on your current level of knowledge of the topics covered in the different courses. There are two parts to the assessment. Each part has 10 multiple
- [17 Key Traits of Data Literacy](https://dataliteracy.com/courses/17-key-traits-of-data-literacy/)
- [Data Literacy Fundamentals Pre-Course Knowledge Check](https://dataliteracy.com/courses/data-literacy-fundamentals-pretest/) - Welcome to the Data Literacy Fundamentals Pre-Course Knowledge Check! The purpose of this knowledge check is to asses your understanding of critical course concepts before going through the sessions themselves. This introductory course helps you become more familiar with the power and value of data, and how teams work together to put it to use.
- [Data Citizen Assessment](https://dataliteracy.com/courses/data-citizen-assessment/) - The Data Citizen Assessment A Data Citizen knows what questions to ask of their data upfront. They understand the one overall goal of data and the activities a team must carry out to transform data into wisdom. Passing the Data Citizen Assessment proves that you’ve mastered the foundational aspects of data literacy: what is data,
- [Visual Interpreter Assessment](https://dataliteracy.com/courses/visual-interpreter-assessment/) - The Visual Interpreter Assessment A visual interpreter is fluent in the visual language of data. They’ve mastered the 25 most common chart types and their pitfalls. They understand how data is converted to graphical marks with visual encodings, the main principles for evaluating the effectiveness of charts, and critical scenarios like change over time and
- [Data Literacy Level 2 Pre-Course Knowledge Check](https://dataliteracy.com/courses/data-literacy-level-2-pre-course-knowledge-check/) - Welcome to the Data Literacy Level 2 Pre-Course Knowledge Check! The purpose of this knowledge check is to assess your understanding of critical course concepts before going through the sessions themselves. Our level 2 course teaches you a tool-agnostic framework to work more effectively with data. The knowledge check has 10 questions and you have
- [Data Literacy Level 2 Post-Course Knowledge Check](https://dataliteracy.com/courses/data-literacy-level-2-post-course-knowledge-check/) - Congratulations on completing the Data Literacy Level 2 Course: Working Effectively Data! Let's perform a quick check of your understanding of some of the main concepts covered in the course. There are 10 questions in this quiz, and you'll have 15 minutes to complete it. Read each question carefully and choose one or more of
- [Data Explorer Assessment](https://dataliteracy.com/courses/data-explorer-assessment/) - The Data Explorer Assessment Savvy Data Explorers are needed by every organization. A Data Explorer understands that every data set has its shortcomings, but that many of them can be used to better understand a situation and ultimately to make better decisions. Passing the Data Explorer Assessment proves that you are proficient in the process
- [Data Citizen Placement Assessment](https://dataliteracy.com/courses/data-citizen-placement-assessment/) - Data Citizen Placement Assessment A Data Citizen knows what questions to ask of their data upfront. They understand the one overall goal of data and the activities a team must carry out to transform data into wisdom. Passing the Data Citizen Assessment proves that you’ve mastered the foundational aspects of data literacy: what is data,
- [Visual Interpreter Placement Assessment](https://dataliteracy.com/courses/visual-interpreter-placement-assessment/) - Visual Interpreter Placement Assessment A visual interpreter is fluent in the visual language of data. They’ve mastered the 25 most common chart types and their pitfalls. They understand how data is converted to graphical marks with visual encodings, the main principles for evaluating the effectiveness of charts, and critical scenarios like change over time and
- [Data Explorer Placement Assessment](https://dataliteracy.com/courses/data-explorer-placement-assessment/) - Data Explorer Placement Assessment Savvy Data Explorers are needed by every organization. A Data Explorer understands that every data set has its shortcomings, but that many of them can be used to better understand a situation and ultimately to make better decisions. Passing the Data Explorer Assessment proves that you are proficient in the process
- [ChatGPT Basics](https://dataliteracy.com/courses/chatgpt-basics/) - In this course, you'll learn all about ChatGPT: what it is, how to use it, and how not to use it. By the time you're done, you'll have a fundamental understanding of the technologies behind this revolutionary tool, and you'll be up and running on the platform.
- [Data Literacy Assessment for Intuit](https://dataliteracy.com/courses/data-literacy-assessment-for-intuit/) - Welcome to this short assessment that is comprised of two parts - the first part is a right-and-wrong quiz with 20 questions and the second part is a subjective survey with 4 questions that provide an opportunity to rate your own level of confidence in four different types of data-working activities. The only purpose of
- [Data Literacy Individual Objective Assessment](https://dataliteracy.com/courses/data-literacy-individual-objective-assessment/) - Welcome to the Data Literacy Individual Objective Assessment! This 20 question assessment tests your comprehension of the foundational principles of data literacy, the ability to read, understand, create, and communicate data. It also tests your ability to read and interpret various types of charts, graphs and maps. Ready to begin? Click 'Data Literacy Individual Objective

## Lessons

- [Lesson 3: Plan](https://dataliteracy.com/courses/advancing-responsible-ai/lessons/lesson-3-plan/) - Core concept This iteration should take you about 30% of your total project time. In this iteration of the IMPACT framework, we’re adding the P for Plan. We'll plan out how you’ll sequence through your data story and what information to include and what not to include. To make this process easier, we'll use a
- [Lesson 3: The DISCOVER Phase](https://dataliteracy.com/courses/data-literacy-level-2-v2/lessons/level-2-lesson-3/) - Learning Objective: By the end of this lesson, you will be able to: Identify the key components of the DISCOVER phase, including data analysis, significance assessment, and iterative refinement of findings. Apply appropriate visualization techniques to analyze data and answer questions, using charts that effectively encode your variables. Evaluate both practical and statistical significance of
- [Introduction](https://dataliteracy.com/courses//lessons/introduction-3/)
- [Introduction](https://dataliteracy.com/courses/harnessing-generative-ai/lessons/introduction-to-harnessing-generative-ai/) - Course Learning Objective By the end of this course, you will be able to: Define what generative AI is, including examples of current technologies. Apply generative AI tools and techniques in practical scenarios, such as content creation and data analysis. Identify and evaluate ethical concerns and biases in AI applications, including potential impacts on privacy and data security. Develop
- [Skills](https://dataliteracy.com/courses/21-key-traits-of-data-ai-literacy/lessons/skills/) - The Skills Category Simply defined, skills are the abilities we possess to do something well. Data literacy doesn’t just involve knowledge about concepts and principles related to data, it also involves the ability to perform tasks and activities that uncover and convey meaning in data. It’s the second group of data literacy traits because it
- [Lesson 6: Test](https://dataliteracy.com/courses/advancing-responsible-ai/lessons/lesson-6-test/) - Core concept This iteration should take you about 10% of your total project time. We're almost there! In this iteration, you’ll bring everything together: your data insight, clear message, thoughtful sequencing, aligned visuals, and polished design. We want to validate the full story and practice your delivery of it. This is an important step because
- [Lesson 4: Align](https://dataliteracy.com/courses/advancing-responsible-ai/lessons/lesson-4-align/) - Core concept This iteration should take you about 30% of your total project time. In this iteration of the IMPACT framework, we’re adding A for Align. Now we have Insight, Message, Plan, Align, Test. The goal of this iteration is to make sure your visuals are aligned with each part of your storyboard. That means
- [Lesson 1: Insight](https://dataliteracy.com/courses/advancing-responsible-ai/lessons/lesson-1-insight/) - Core concept This iteration should take you about 10% of your total project time. First, define the purpose of your data story. Are you looking for... a decision to be made an action to be taken simply to share information Knowing the purpose of your data story will help you align every part of your
- [Conclusion to Data Storytelling for IMPACT](https://dataliteracy.com/courses/advancing-responsible-ai/lessons/conclusion-to-data-storytelling-for-impact/) - Core concept You made it to the end of Data Storytelling for IMPACT! I hope you’re feeling more confident, more creative, and more in control of how you communicate with data. Let’s take a moment to recap everything you’ve accomplished through the IMPACT framework: Insight: You explored your data and identified what’s worth sharing. Message:
- [Introduction to Data Storytelling for IMPACT](https://dataliteracy.com/courses/advancing-responsible-ai/lessons/introduction-to-data-storytelling-for-impact/) - Introduction Welcome to Data Storytelling for IMPACT! This course will teach you how to use a repeatable, tool-agnostic framework that you can use over and over again. Use this framework to increase your chances of creating a data story that's relevant to your audience and leads to a decision or action. Creating a data story
- [Lesson 5: Clarify](https://dataliteracy.com/courses/advancing-responsible-ai/lessons/lesson-5-clarify/) - Core concept This iteration should take you about 10% of your total project time. Now it’s time to clarify. That means we’re polishing the design of your story so that every element is clear, consistent, and reinforces your main message. In this iteration of the IMPACT framework, we’re adding the C for Clarify: Insight, Message,
- [Lesson 2: Message](https://dataliteracy.com/courses/advancing-responsible-ai/lessons/lesson-2-message/) - Core concept This iteration should take you about 10% of your total project time. This iteration is dedicated to creating a relevant message for your audience. Message Step 1 Step 2 Step 3 Step 4 Prioritize who will be in the audience when you share your data story. This prioritization will help you make decisions
- [Introduction to Data Literacy Level 2](https://dataliteracy.com/courses/data-literacy-level-2-v2/lessons/introduction-to-data-literacy-level-2-v2/) - Welcome, Data Literacy Level 2: Working with Data, where you'll learn how to transform raw data into useful insights and decisions. At the heart of this course is the WISDOM Data-Working Flow—a comprehensive framework that guides you through the entire data analysis process.
- [Lesson 1: The WONDER Phase](https://dataliteracy.com/courses/data-literacy-level-2-v2/lessons/level-2-lesson-1/) - Learning Objective: By the end of this lesson, you will be able to: Identify the key components of the WONDER phase as the starting point of the data analysis process, including observation, questioning, hypothesis formation, and data collection. Develop skills for making keen observations about data and the world it represents, while recognizing common cognitive
- [Lesson 5: AI Benefits and Concerns](https://dataliteracy.com/courses/ai-literacy-fundamentals/lessons/ai-benefits-and-concerns/) - It is a mistake to suppose that any technological innovation has a one-sided effect. Every technology is both a burden and a blessing; not either-or, but this-and-that. - Neil Postman, American author, educator, and cultural critic Blessings and Burdens Let's pivot from the historical and the technical considerations of AI to the personal and the
- [Lesson 2: A Brief History of AI](https://dataliteracy.com/courses/ai-literacy-fundamentals/lessons/a-brief-history-of-ai/) - Machines will be capable, within twenty years, of doing any work that a man can do. - Herbert A. Simon, 1960 Timeline of Significant Milestones in the Evolution of AI Despite ancient myths and legends about machines that behave intelligently, most AI experts trace the history of the field of AI back to a 1956
- [Wrapping up the Course](https://dataliteracy.com/courses/data-literacy-level-1-v2/lessons/wrapping-up-data-literacy-level-1-v2/) - Wrapping Up the Course Congratulations on completing Learning to See Data! You've taken a significant step toward mastering the visual language of data, equipping yourself with the knowledge to interpret and communicate data more effectively. Let's recap what you've learned and how you can apply these skills moving forward. Course Highlights The Building Blocks of
- [Wrapping Up Level 2](https://dataliteracy.com/courses/data-literacy-level-2-v2/lessons/wrapping-up-data-literacy-level-2-v2/) - Wrapping Up Level 2 Congratulations on completing Data Literacy Level 2: The WISDOM Framework for Data Analysis! YYou’ve taken an important step toward mastering not just data tools—but data thinking. You’ve learned how to work with raw data, ask powerful questions, and drive meaningful decisions. Now it’s time to reflect on the journey and prepare
- [Lesson 2: The SHAPE Phase](https://dataliteracy.com/courses/data-literacy-level-2-v2/lessons/level-2-lesson-2/) - Learning Objective: By the end of this lesson, you will be able to: Identify the key components of the SHAPE phase, including exploring data contours, identifying shortcomings, and preparing data for analysis. Evaluate data quality by examining its granularity, size, structure, distributions, and limitations to identify potential issues before analysis. Recognize common data shortcomings including
- [Lesson 4: The MATURE Phase](https://dataliteracy.com/courses/data-literacy-level-2-v2/lessons/level-2-lesson-4/) - Learning Objective: By the end of this lesson, you will be able to: Identify the key components of the MATURE phase, including message crafting, feedback collection, and implementation of data-driven decisions. Craft compelling data presentations tailored to your audience that highlight key insights and incorporate effective visuals and annotations. Implement strategies for gathering and responding
- [Introduction](https://dataliteracy.com/courses/data-literacy-fundamentals-v2/lessons/introduction-to-fundamentals/) - Welcome to Data Literacy Fundamentals! This course is designed to take you through eight different lessons that teach you the power and value of data. There are no prerequisites for the course, and learners of any level can dive right in. If you’re relatively new to data, or even somewhat “dataphobic,” then this course is
- [Lesson 6: Ethical Considerations of Generative AI](https://dataliteracy.com/courses/harnessing-generative-ai/lessons/lesson-6-ethical-considerations-of-generative-ai/) - Key Ethical Concerns Generative AI, with its ability to create content that can sometimes be indistinguishable from human-created work, brings with it a myriad of complex and evolving ethical concerns. As we harness the potential of these technologies, it's essential that we stay mindful of their possible impacts on individuals and society. Let's explore five
- [1. What Do Data Professionals Do?](https://dataliteracy.com/courses/working-with-data-professionals/lessons/1-what-do-data-professionals-do/) - Learning Objective: By the end of this lesson, learners will be able to identify and describe the primary skill sets of data professionals, including Data Engineering, Reporting, Data Analysis, Experimentation, Machine Learning, and Visualization and Storytelling, in order to effectively manage and collaborate with data teams. Activity Flow of Data Work It's all too common for
- [Knowledge](https://dataliteracy.com/courses/21-key-traits-of-data-ai-literacy/lessons/knowledge/) - The Knowledge Category Our knowledge is the body of facts and information with which we are aware or familiar. It’s the first category of data literacy traits enumerated because our development starts with the knowledge that we obtain either through academic study or through practical experience. So, what does a data literate person know? A
- [Attitudes](https://dataliteracy.com/courses/21-key-traits-of-data-ai-literacy/lessons/attitudes/) - The Attitudes Category Attitudes are ways of thinking or feeling that often affect how we behave. Our attitudes stem from our knowledge and skills, and are also shaped by our interactions with others. It’s possible to know a great deal about data and build many powerful skills, and yet still be held back by unhelpful
- [Behaviors](https://dataliteracy.com/courses/21-key-traits-of-data-ai-literacy/lessons/behaviors/) - The Behaviors Category Our behaviors are the ways in which we act or conduct ourselves in the world. This is the final category of data literacy traits because our actions are the outcome of our knowledge, skills and attitudes, and how we ultimately make a difference with data. The other three categories don’t amount to
- [Course Overview](https://dataliteracy.com/courses/21-key-traits-of-data-ai-literacy/lessons/course-overview/) - Course Overview There are many different traits that we can acquire on our data & AI literacy journey, from wrangling raw data tables to creating clear and compelling charts and graphs to finding ways to continuously improve the data itself – and many more! In this course, we'll review a list of some of the most
- [Conclusion & Growth Plan](https://dataliteracy.com/courses/21-key-traits-of-data-ai-literacy/lessons/conclusion-2/) - Conclusion Congratulations on finishing the 21 Key Traits of Data & AI Literacy course and self-assessment! We covered a lot of ground! There's no denying that a list of 21 traits is a lot to consider. Luckily, we don’t have to master each one. We work in teams so that we can complement each other, lean on
- [Wrapping Up the Course](https://dataliteracy.com/courses/data-literacy-fundamentals-v2/lessons/wrapping-up/) - "And this is the beginning of the end." - Guy Kawasaki Final Thoughts We covered a lot of ground in this course, so congratulations on finishing this phase of your data literacy journey! The goals have been to demystify data, to help us see that we are already an active participant in the data revolution
- [Lesson 1: What is Generative AI?](https://dataliteracy.com/courses/harnessing-generative-ai/lessons/lesson-1-what-is-generative-ai/) - What is Generative AI? Generative AI, sometimes referred to as GenAI, refers to a class of artificial intelligence that's capable of creating new content based on the vast amounts of data it has been trained on. This content can take various forms such as text, images, audio, and videos. At its core, generative AI uses machine
- [Lesson 2: The Technology Behind Generative AI](https://dataliteracy.com/courses/harnessing-generative-ai/lessons/lesson-2-the-technology-behind-generative-ai/) - The Deep Learning Revolution Emergent Abilities Generative AI represents a significant leap in AI capabilities. Unlike the earliest AI models from the mid-20th century that required explicit programming in order to perform predefined tasks, modern generative AI models are based on a newer branch of AI called machine learning, which allows systems to learn patterns and
- [CARE](https://dataliteracy.com/courses/chart-spark/lessons/care/) - Watch this video, and then read or listen to the information below it. Listen to the audiobook for this section Before you dismiss the idea of caring for your creativity as vague and squishy, take a moment to recognize a potential consequence of not caring for it: BURNOUT. You can relentlessly draw ideas from your
- [Part 1 - Knowledge](https://dataliteracy.com/courses/17-key-traits-of-data-literacy/lessons/part-1-knowledge/) - The Knowledge Category Our knowledge is the body of facts and information with which we are aware or familiar. It’s the first category of data literacy traits enumerated because our development starts with the knowledge that we obtain either through academic study or through practical experience. What does a data literate person know? A data
- [Lesson 1: What is AI?](https://dataliteracy.com/courses/ai-literacy-fundamentals/lessons/what-is-ai/) - It is the science and engineering of making intelligent machines, especially intelligent computer programs. - John McCarthy, Stanford professor who coined the term in 1955 (also known as the "father" of AI) Defining AI Let's start by coming up with a good definition for artificial intelligence, or AI. It's surprisingly difficult to define AI, at
- [1. W: The WONDER Phase](https://dataliteracy.com/courses/data-literacy-level-2/lessons/1-w-the-wonder-phase/) - Introduction to Phase 1: The WONDER Phase Welcome to the first phase of the WISDOM Data-Working Flow! The first phase is called the WONDER Phase. In this phase, you'll start off on the right foot by thinking clearly about your situation and your data. The steps in this first phase are often glossed over or
- [Lesson 8: Interpreting Geographic Data with Symbol Maps](https://dataliteracy.com/courses/data-literacy-level-1-v2/lessons/level-1-lesson-8/) - Learning Objective: By the end of this lesson, you will be able to: Identify the key building blocks of all symbol maps, like latitude, longitude, projection, and the symbol. Recognize effective use cases for symbol maps, like showing geographical patterns. Analyze common issues with symbol maps, like land area distortion caused by map projections and
- [Lesson 7: Finding Correlations with Scatter Plots](https://dataliteracy.com/courses/data-literacy-level-1-v2/lessons/level-1-lesson-7/) - Learning Objective: By the end of this lesson, you will be able to: Identify the key building blocks of all scatterplots, like explanatory variable, response variable, observation, and trend line. Recognize effective use cases for scatterplots, like showing the relationship between variables. Analyze common issues with scatterplots, like overplotting. Distinguish variations of scatterplots and their
- [Lesson 6: Understanding Variations with Histograms](https://dataliteracy.com/courses/data-literacy-level-1-v2/lessons/level-1-lesson-6/) - Learning Objective: By the end of this lesson, you will be able to: Identify the key building blocks of all histograms, like bars, bins, and count. Recognize effective use cases for histograms, like showing distribution or variation. Analyze common issues with histograms, like choosing an appropriate bin size. Distinguish variations of histograms and their application,
- [Lesson 5: Seeing Change Over Time with Line Charts](https://dataliteracy.com/courses/data-literacy-level-1-v2/lessons/level-1-lesson-5/) - Learning Objective: By the end of this lesson, you will be able to: Identify the key building blocks of all line charts, like lines, axes, labels, and scales. Recognize effective use cases for line charts, like showing trends over time. Analyze common issues with line charts, like too many lines or categories. Distinguish variations of
- [Lesson 2: Enhancing Data Tables with Heatmaps](https://dataliteracy.com/courses/data-literacy-level-1-v2/lessons/level-1-lesson-2/) - Learning Objective: By the end of this lesson, you will be able to: Identify the key building blocks of heatmap tables, like rows, columns, and color. Recognize effective use cases for heatmap tables, like providing an overview of a high density table. Analyze common issues with heatmap tables, like inaccuracies in interpreting color scales. Distinguish
- [Lesson 3: Comparing Quantities with Bar Charts](https://dataliteracy.com/courses/data-literacy-level-1-v2/lessons/level-1-lesson-3/) - Learning Objective: By the end of this lesson, you will be able to: Identify the key building blocks of all bar charts, like bars, axes, labels, and scales. Recognize effective use cases for bar charts, like comparing magnitude across categories. Analyze common issues with bar charts, like misleading scales. Distinguish variations of bar charts and
- [Lesson 4: Relating Part-to-Whole with Pie Charts](https://dataliteracy.com/courses/data-literacy-level-1-v2/lessons/level-1-lesson-4/) - Learning Objective: By the end of this lesson, you will be able to: Identify the key building blocks of all pie charts, like area and angle. Recognize effective use cases for pie charts, like showing part-to-whole relationships. Analyze common issues with pie charts, like too many slices. Distinguish variations of pie charts and their application,
- [Introduction to Data Literacy Level 1: Learning to See Data](https://dataliteracy.com/courses/data-literacy-level-1-v2/lessons/introduction-to-data-literacy-level-1-v2/) - Course Learning Objective: By the end of this lesson, you will be able to: Identify the key building blocks of the most common data visualizations including heatmap tables, bar charts, pie charts, line charts, histograms, scatter plots, and maps. Assess the most effective use cases for each type of visualization, such as using pie charts
- [5. Creating Prompts for ChatGPT](https://dataliteracy.com/courses/chatgpt-basics/lessons/4-creating-prompts-for-chatgpt/) - Learning Objective: By the end of this lesson, you'll understand the differences between chatting with ChatGPT and searching the internet, and you'll master the four steps to effectively interact with ChatGPT: 1) crafting clear, specific, and open-ended prompts, 2) waiting for responses, 3) reviewing and fact-checking generated answers, and 4) repeating the process for ongoing
- [1. The Rise of ChatGPT](https://dataliteracy.com/courses/chatgpt-basics/lessons/1-the-rise-of-chatgpt/) - Learning Objective: By the end of this lesson, you'll understand the role of major players in the AI chatbot market and you'll be able to evaluate the rapid pace of development and competition in this product category. You'll also have actually used ChatGPT, perhaps for the first time! By now, it has become clear to
- [4. Getting Started with ChatGPT](https://dataliteracy.com/courses/chatgpt-basics/lessons/3-getting-started-with-chatgpt/) - Learning Objective: By the end of this lesson, you'll be able to set up and access ChatGPT through various platforms, including OpenAI's website, Microsoft's "new Bing," Data Literacy website, and other apps or messaging services, to prepare for using the AI tool effectively. Before you learn how to use ChatGPT, you’ll need to get set
- [3. ChatGPT Warnings and Caveats](https://dataliteracy.com/courses/chatgpt-basics/lessons/3-chatgpt-warnings-and-caveats/) - Learning Objective: By the end of this lesson on ChatGPT Basics, learners will be able to identify and discuss key ethical concerns and limitations associated with using ChatGPT, such as copyright infringement, biased responses, and incorrect responses, and demonstrate an understanding of the importance of cautious and responsible use of this AI technology. Every new
- [2. What Is ChatGPT?](https://dataliteracy.com/courses/chatgpt-basics/lessons/2-what-is-chatgpt/) - Learning Objective: By the end of this lesson, you'll will be able to define ChatGPT, describe its origins and development, understand the underlying concepts of natural language processing, neural networks, and large language models, and identify potential applications and limitations of using ChatGPT in various tasks. In order to adapt to this new technology, it’s
- [Introduction to ChatGPT Basics](https://dataliteracy.com/courses/chatgpt-basics/lessons/introduction-to-chatgpt-basics/) - Course Introduction Welcome to ChatGPT Basics, an introductory course on how to use the powerful new AI chatbot by OpenAI. This course should take from 1 to 2 hours to complete. To claim your course completion certificate, read through each of the lesson pages, try out the exercise prompts, and complete each of the quizzes
- [Wrapping Up the Course](https://dataliteracy.com/courses/working-with-data-professionals/lessons/wrapping-up-working-with-data-professionals/) - Summary and Conclusion We’ve covered a lot of ground and explored various topics in this book! Any one of these lesson could be a course on its own but it’s our hope that by distilling them into easily consumable concepts and advice, you’ll find your footing in what can sometimes feel like an overwhelming situation.
- [5. Up-Leveling Your Partnership with Data Professionals](https://dataliteracy.com/courses/working-with-data-professionals/lessons/5-building-a-partnership-with-data-professionals/) - Learning Objective: By the end of this lesson, learners will be able to understand and apply strategies for building a partnership with data professionals, focusing on leading with context, leaning on their expertise, and closing the loop. Three Tips to Up-Level Your Partnership with Data Professionals A relationship where you brainstorm a problem, decide how
- [4. Measuring the Performance of Data Professionals](https://dataliteracy.com/courses/working-with-data-professionals/lessons/4-measuring-the-performance-of-data-professionals-parts-1-2/) - Learning Objective: By the end of this lesson, learners will be able to evaluate the performance of data professionals by assessing work outputs and individual performance, focusing on quality, fit, execution, impact, and influence. Evaluating Work Outputs & Individual Performance One of the ironies of professional data work is that the primary outcome of the work
- [3. Managing a Team of Data Professionals](https://dataliteracy.com/courses/working-with-data-professionals/lessons/3-managing-a-team-of-data-professionals/) - Learning Objective: By the end of this lesson, learners will be able to implement effective management strategies for leading a team of data professionals, even if they do not have a background in data themselves. Four Critical Reminders for Managers of Data Teams Being a people leader of a team of data professionals involves many of
- [2. How to Hire a Data Professional](https://dataliteracy.com/courses/working-with-data-professionals/lessons/2-how-to-hire-a-data-professional-v-2-0/) - Learning Objective: By the end of this lesson, learners will be able to outline a strategic process for hiring data professionals that aligns with their organization's goals and ensures the right skill sets are in place for successful data projects. Things to Consider Before Hiring Hiring a data professional can be a bit tricky if you
- [Introduction to Working with Data Professionals (even if you aren't one!)](https://dataliteracy.com/courses/working-with-data-professionals/lessons/introduction-to-working-with-data-professionals-even-if-you-arent-one/) - Course Introduction Whether your background is in the data field or something completely unrelated, it can be challenging to know how to set up your organization’s data efforts for success. Much of that success starts with the people you have working on these efforts. Hiring the right data professionals for the right data job is
- [Lesson 1: The Building Blocks of All Charts](https://dataliteracy.com/courses/data-literacy-level-1-v2/lessons/level-1-lesson-1/) - Learning Objective: By the end of this lesson, you will be able to: Grasp how the human visual system functions and its role in interpreting charts, including common cognitive biases that affect our perception of visual information. Identify the key building blocks of the most common data visualizations including heatmap tables, bar charts, pie charts,
- [Wrapping up the Course](https://dataliteracy.com/courses/data-literacy-for-leaders/lessons/wrapping-up-the-course/) - Final Thoughts I hope that at this point, you appreciate that being a great leader in the age of data involves more than just giving your team members access to clean data and powerful technologies. You need to provide them with those things, but you also need to establish sound principles of ethics, connect the data to your team’s purpose,
- [The CULTURE Factor](https://dataliteracy.com/courses/data-literacy-for-leaders/lessons/the-culture-factor/) - Learning Objective: Upon completion of Lesson 7: The CULTURE Factor, you'll be equipped to evaluate and enhance your team's data culture. You'll learn to nurture data-informed communities, integrate data into your team's identity, and reward data successes. You'll gain skills to implement data-focused learning opportunities, improve communication, and use data for culture enhancement. Finally, you'll be
- [The PROCESS Factor](https://dataliteracy.com/courses/data-literacy-for-leaders/lessons/the-process-factor/) - Learning Objective: Upon completion of Lesson 6: The PROCESS Factor, you'll acquire the skills to use data-driven strategies to improve team efficiency. You'll understand how to streamline recurring tasks, manage data access rights, and leverage data in decision-making. Furthermore, you'll be equipped to create and adapt Agile processes, cultivating a culture of continuous improvement, thereby propelling
- [The PEOPLE Factor](https://dataliteracy.com/courses/data-literacy-for-leaders/lessons/the-people-factor/) - Learning Objective: After completing Lesson 5: The PEOPLE Factor, you'll be equipped to foster data literacy within you team, promote effective data visualization and communication, and identify data-related pitfalls. You'll gain skills to implement a culture of data-informed decision-making, oversee the creation of a data-wise team, and advocate for necessary data training, thereby enhancing your leadership
- [The TECHNOLOGY Factor](https://dataliteracy.com/courses/data-literacy-for-leaders/lessons/the-technology-factor/) - Learning Objective: Upon completing Lesson 4: The TECHNOLOGY Factor, you'll recall and grasp the seven guiding principles of Technology, be able to evaluate your own team's data tools, and identify points of friction to alleviate. You'll devise strategies for optimizing data accessibility, promoting interoperability, and facilitating self-service analytics. You will also develop plans for adoption of
- [The 8 Questions to Ask Upfront](https://dataliteracy.com/courses/data-literacy-fundamentals-v2/lessons/eight-questions-to-ask-upfront/) - Learning Objective: By the end of this lesson, you will be able to: Assess and analyze data sets and visualizations using 'The EIGHT Questions to Ask Upfront.' Critically evaluate the relevance, source, ownership, currency, key variables, definitions, collection methods, and time considerations of data, enabling you to make informed decisions and develop deeper insights in
- [The 7 Groups of Data Activities](https://dataliteracy.com/courses/data-literacy-fundamentals-v2/lessons/seven-groups-of-data-activities/) - Learning Objective: By the end of this lesson, you will be able to: Accurately identify and explain the seven groups of data activities: creating data, building data sources, preparing data, analyzing data, presenting data, consuming data, and making data-informed decisions. Understand the role and importance of each activity in the data lifecycle, recognizing how they
- [The 5 Forms of Data Analysis](https://dataliteracy.com/courses/data-literacy-fundamentals-v2/lessons/five-forms-of-data-analysis/) - Learning Objective: By the end of this lesson, you will be able to: Identify and describe the five forms of data analysis: descriptive, inferential, diagnostic, predictive, and prescriptive. Distinguish between these five forms of data analysis through examples and exercises, understanding when and how each form is used effectively in data analysis. Apply these concepts
- [The DATA Factor](https://dataliteracy.com/courses/data-literacy-for-leaders/lessons/the-data-factor/) - Learning Objective: Upon completion of Lesson 3: The DATA Factor, you'll be able to explain the seven guiding principles of Data and their role in driving organizational success. You'll be able to apply these principles to secure, improve and optimize data access within your team. You will also develop the ability to evaluate data quality and
- [The 2 Systems of Thinking](https://dataliteracy.com/courses/data-literacy-fundamentals-v2/lessons/two-systems-of-thinking/) - Learning Objective: By the end of this lesson, you will be able to: Differentiate between system 1 and system 2 thinking. Identify types of thought associated with each system of thinking. Apply your knowledge to use both types of thinking when working with data. Identify common cognitive biases that might arise with each type of
- [The 3 Domains of Application](https://dataliteracy.com/courses/data-literacy-fundamentals-v2/lessons/three-domains-of-application/) - Learning Objective: By the end of this lesson, you will be able to: Identify and describe the three domains of data application: personal, public, and professional. Analyze and differentiate between these domains through specific examples and case studies. Identify the purpose of the Goal Tree technique, and apply it to break down high-level Key Performance
- [The PURPOSE Factor](https://dataliteracy.com/courses/data-literacy-for-leaders/lessons/the-purpose-factor/) - Learning Objective: Upon completion of Lesson 2: The PURPOSE Factor, you'll be able to recall and explain the seven guiding principles of Purpose, apply these principles to define your own team's aspirations and goals, create relevant and aligned performance metrics, and flow these high-level metrics down into relevant objectives for your various team members. "Efforts and
- [The ETHICS Factor](https://dataliteracy.com/courses/data-literacy-for-leaders/lessons/the-ethics-factor/) - Learning Objective: Upon completion of Lesson 1: The ETHICS Factor, you'll be able to recall the seven guiding principles for ethical data practices, understand the concept of data ethics and its relevance to leadership, apply these principles to real-world scenarios, analyze your own track record relative to data ethics, and create strategies to promote ethical data
- [Introduction to Data Literacy for Leaders](https://dataliteracy.com/courses/data-literacy-for-leaders/lessons/introduction/) - Course Introduction Welcome to the Data Literacy for Leaders course! It’s imperative in today’s world that leaders of organizations of all types and sizes be data-savvy. This is true no matter what department or industry leaders find themselves in. As a leader, you can either try to ignore data, or you can embrace it, harness it, and
- [The 6 Ways of Displaying Data](https://dataliteracy.com/courses/data-literacy-fundamentals-v2/lessons/six-ways-of-displaying-data/) - Learning Objective: By the end of this lesson, you will be able to: Identify and describe the six primary ways of displaying data: figures, tables, statistics, visualizations, dashboards, and data stories. Demonstrate the ability to select and apply the most appropriate data display method for various types of data sets and scenarios. Critically analyze the
- [The 4 Types of Data Scales](https://dataliteracy.com/courses/data-literacy-fundamentals-v2/lessons/four-types-of-data-scales/) - Learning Objective: By the end of this lesson, you will be able to: Differentiate between categorical and quantitative data. Identify and explain the four types of data scales: nominal, ordinal, interval, and ratio. Classify various data examples into these categories accurately. Understand the implications of each scale type for data analysis and interpretation. Perhaps agreement
- [Lesson 4: AI Safety and Security](https://dataliteracy.com/courses/advancing-responsible-ai/lessons/lesson-4-ai-safety-and-security/) - AI Safety focuses on ensuring that AI systems behave as intended, minimizing the risk of unintended harm. In contrast, AI Security involves safeguarding AI systems against malicious attacks and misuse. Both aspects are essential for developing reliable and trustworthy AI systems. Challenges to AI Safety As AI technology continues to evolve and integrate into various
- [Wrapping Up the Course](https://dataliteracy.com/courses/advancing-responsible-ai/lessons/wrapping-up-advancing-responsible-ai/) - Reflecting on Our Journey: Advancing Responsible AI Throughout this course, you’ve immersed yourself in the core principles that form the foundation of responsible AI. These lessons provide you with not just theoretical insights but actionable frameworks that will empower you to actively shape the future of AI development and deployment. By mastering these principles, you're
- [Lesson 6: AI's Impact on Society and the Environment](https://dataliteracy.com/courses/advancing-responsible-ai/lessons/lesson-6-ais-impact-on-society-and-the-environment/) - AI for Social Good involves leveraging artificial intelligence to tackle societal challenges and enhance the overall quality of life. When developing AI for social good, consider the following questions: What is the clear purpose of this AI system that I'm developing? How can I ensure this AI is inclusive and accessible to all potential users?
- [Lesson 5: Accountability and AI Governance](https://dataliteracy.com/courses/advancing-responsible-ai/lessons/lesson-5-accountability-and-ai-governance/) - To build AI systems we can trust, we must hold them accountable—ensuring they are not only powerful, but governed by ethical principles that protect society and promote transparency. AI Accountability and AI Governance AI accountability focuses on answering a critical question: "Who is responsible for the decisions and actions made by AI systems?" It emphasizes
- [Lesson 3: Privacy and Data Protection in AI](https://dataliteracy.com/courses/advancing-responsible-ai/lessons/lesson-3-privacy-and-data-protection-in-ai/) - Privacy in artificial intelligence focuses on protecting individuals' personal information from unauthorized access and misuse. This includes identifiable information like names and addresses, as well as less obvious data such as internet browsing history, recent online purchases, and social media connections. Data Protection Laws Government agencies worldwide are recognizing the critical need for robust data
- [Lesson 2: Transparent and Explainable AI](https://dataliteracy.com/courses/advancing-responsible-ai/lessons/lesson-2-transparent-and-explainable-ai/) - Explainable AI (XAI) refers to artificial intelligence systems and models that are designed to be transparent, interpretable, and understandable by humans. Transparency and Explainability in AI Transparency in AI involves clear visibility into the system's data, algorithms, and processes, including its architecture and training methodologies. Explainability, on the other hand, focuses on understanding how AI
- [The 1 Overall Goal of Data](https://dataliteracy.com/courses/data-literacy-fundamentals-v2/lessons/one-overall-goal-of-data/) - Learning Objective: By the end of this lesson, you will be able to: Recall the correct form of the DIKW Pyramid. Match each of the four levels of the DIKW pyramid with its corresponding description. Evaluate different situations and recognize the associated level of the DIKW Pyramid. Apply your knowledge to construct a plan to
- [Lesson 1: Bias and Fairness in AI](https://dataliteracy.com/courses/advancing-responsible-ai/lessons/lesson-1-bias-and-fairness-in-ai/) - Fairness in AI is about minimizing the degree to which our intelligent systems discriminate based on race, gender, age, or other protected attributes. Achieving fairness is an ongoing commitment. It must be integrated into every stage of the AI development lifecycle, from assembling diverse teams to setting ethical guidelines and continuously monitoring systems to ensure fairness. Forms
- [Introduction](https://dataliteracy.com/courses/advancing-responsible-ai/lessons/introduction-to-advancing-responsible-ai/) - Introduction Artificial Intelligence is transforming industries, enhancing productivity, and reshaping the way we interact with the world. From healthcare to finance, AI has become a driving force in innovation. However, as AI systems become more integrated into critical systems, the need for ethical oversight has never been more urgent. Advancing Responsible AI is designed to
- [Wrapping Up the Course](https://dataliteracy.com/courses/harnessing-generative-ai/lessons/wrapping-up-harnessing-generative-ai/) - Wrapping Up the Course Congratulations on completing the Harnessing Generative AI course! We have journeyed together through the fascinating and complex world of generative AI, covering its underlying technologies, applications, and ethical considerations. Let's recap what you've learned and how you can move forward with this powerful technology. Course Highlights: Understanding Generative AI: You've learned what
- [Lesson 5: Avoiding Generative AI Pitfalls](https://dataliteracy.com/courses/harnessing-generative-ai/lessons/lesson-5-avoiding-generative-ai-pitfalls/) - Understanding AI Hallucinations What are hallucinations? According to Ziwei Ji and fellow authors of the paper “Survey of Hallucination in Natural Language Generation,” a hallucination is “generated content that is nonsensical or unfaithful to the provided source content." They: Give the impression of being fluent and natural. Appear to be grounded in the real context provided. Are
- [Lesson 4: Strategic Applications of Generative AI](https://dataliteracy.com/courses/harnessing-generative-ai/lessons/lesson-4-strategic-applications-of-generative-ai/) - Strategic versus Tactical Applications Strategic applications have a broad, transformative impact on an organization and provide key, competitive advantages. Example: Implementing a personalized, AI-driven learning platform. Tactical applications are narrower in scope and often improve specific processes. Example: Generating quiz questions for a training course. The Jagged Technological Frontier The Jagged Technological Frontier, a concept introduced in 2023 by scholars
- [Lesson 3: Effective Prompting of Generative AI](https://dataliteracy.com/courses/harnessing-generative-ai/lessons/lesson-3-effective-prompting-of-generative-ai/) - Prompts A prompt is the input you provide to guide the output of a generative AI model. It's how you communicate your intentions to the AI. A typical prompt consists of four main components: Instruction: What you want the AI to do. Context: Background information. Examples: Sample inputs and outputs. The Question: Your specific task or query. Effective Prompting Effective prompting is an iterative process. By
- [Wrapping Up the Course](https://dataliteracy.com/courses/ai-literacy-fundamentals/lessons/wrapping-up-ai-literacy-fundamentals/) - Congratulations! We’ve arrived at the end of the beginning of your journey into AI. We’ve covered a lot of ground together, and you’re emerging as an “AI Citizen,” knowledgeable about basic definitions, everyday applications, the circuitous history, the core technologies, the many benefits and harms, the colorful myths and misconceptions, and the basic, balanced truths
- [Lesson 6: AI Myths and Truths](https://dataliteracy.com/courses/ai-literacy-fundamentals/lessons/ai-benefits-and-concerns-2/) - AI might be a powerful technology, but things won’t get better simply by adding AI. - Vivienne Ming, American neuroscientist Let’s consider 10 common myths and misconceptions about AI. We’ll divide each of the 10 into a version of the myth believed by those who are overly optimistic about AI, and a version believed by
- [Lesson 4: A Primer on Deep Learning](https://dataliteracy.com/courses/ai-literacy-fundamentals/lessons/a-primer-on-deep-learning/) - The tools and technologies we’ve developed are really the first few drops of water in the vast ocean of what AI can do. - Fei-Fei Li, the “godmother” of AI What Is Deep Learning? Deep learning is a subset of machine learning, which in turn is a subset of AI; A world within a world within a world.
- [Conclusion](https://dataliteracy.com/courses/chart-spark/lessons/conclusion/) - Watch this video, and then read or listen to the information below it. Don't forget to download your Spark Journal so it's handy during your next project! Listen to the audiobook for this section Here we are at the end, but it’s really the beginning, isn’t it? Do you remember, just a handful of lessons
- [COMMUNICATE](https://dataliteracy.com/courses/chart-spark/lessons/communicate/) - Watch this video, and then read or listen to the information below it. Listen to the audiobook of this section Now that you have some tools to care for and coax out your creativity, the next set of tools in your creativity toolbox will help you shape your ideas into an effective message. In our
- [COAX](https://dataliteracy.com/courses/chart-spark/lessons/coax/) - Watch this video, and then read or listen to the information below it. Listen to the audiobook of this section ⚡“You’re not blocked; you’re using the wrong prompt.” — Melanie Deziel, author of Prove It38 Now that we’ve learned a few essential elements to care for your creativity (practice being more open, cultivate a balanced
- [Lesson 3: Machine Learning Basics](https://dataliteracy.com/courses/ai-literacy-fundamentals/lessons/machine-learning-basics/) - What we want is a machine that can learn from experience. - Alan Turing, Lecture to the London Mathematical Society, on February 20, 1947 What Is Machine Learning? Think of someone you know who is very intelligent. That person is most likely a fast learner, right? The very notion of intelligence is linked closely with
- [Introduction to AI Literacy Fundamentals](https://dataliteracy.com/courses/ai-literacy-fundamentals/lessons/introduction-to-ai-literacy-fundamentals/) - Course Learning Objective By the end of this course, you will be able to: Understand the foundational concepts and definitions of AI List the key figures and historical developments of the field of AI Distinguish between different types of machine learning Understand the basic concepts behind deep learning Critically evaluate AI applications in various domains, and Debunk common myths, fostering a balanced perspective on AI's potential
- [Introduction](https://dataliteracy.com/courses/chart-spark/lessons/introduction-2/) - Welcome! I'm so glad you're here. I’m Alli Torban—an information designer and data literacy advocate based in Washington, DC. I’m also the host of the popular podcast Data Viz Today. This probably isn’t the first investment you've made in your creativity. I don’t know about you, but I’m tired of hearing creativity be conflated with artistry
- [17 Key Traits Self-Assessment](https://dataliteracy.com/courses/17-key-traits-of-data-literacy/lessons/17-key-traits-self-assessment/) - Congratulations on Evaluating all 17 Traits! You've made it through all 17 topic pages in the 4 categories of Knowledge, Skills, Attitudes and Behaviors. You now have a handle on these 17 traits, and you've given some thought to how proficient you currently are in each of these traits as well as how important they
- [Level 2 Course Introduction](https://dataliteracy.com/courses/data-literacy-level-2/lessons/level-2-course-introduction/) - Course Introduction Welcome to the Data Literacy Level 2 course! In this on-demand version of the course, you'll learn a step-by-step process that will help you work effectively with data. The process is called the WISDOM Data-Working Flow, and you can use it with any data set, any software tool or programming language, any question
- [2. S: The SHAPE Phase](https://dataliteracy.com/courses/data-literacy-level-2/lessons/2-s-the-shape-phase/) - Introduction to Phase 2: The SHAPE Phase Okay, you've made it through the first full phase of the process, and you now have relevant data in your possession. With that data in hand, you're ready to step through the second phase, which makes up the S of the WISDOM Data-Working Flow, and stands for SHAPE.
- [3. D: The DISCOVER Phase](https://dataliteracy.com/courses/data-literacy-level-2/lessons/3-d-the-discover-phase/) - Introduction to Phase 3: The DISCOVER Phase Okay, you’ve made it to the DISCOVER Phase! The goal of this phase in the process is to explore your data and to glean insights that will help you move forward. In this phase, you'll combine your knowledge of the power and value of data, your fluency in
- [4. M: The MATURE Phase](https://dataliteracy.com/courses/data-literacy-level-2/lessons/4-m-the-mature-phase/) - Introduction to Phase 4: The MATURE Phase Alright, you’ve made it all the way to the MATURE Phase! In this fourth and final phase of the WISDOM Data-Working Flow, you'll learn how to craft and deliver a notable message, how to solicit and pay close attention to your audience’s feedback and reactions, and how to
- [Data Literacy Level 1 - Course Introduction](https://dataliteracy.com/courses/data-literacy-level-1/lessons/data-literacy-level-1-course-introduction/) - Course Introduction Welcome to the Data Literacy Level 1 course! This course will teach you how to read and interpret graphical displays of data with confidence. There are no prerequisites for the course, but the Data Literacy Fundamentals course is a helpful primer for those who are newest to interacting with data. Every single person
- [Data Literacy Fundamentals Course Introduction](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/fundamentals-course-introduction/) - Course Introduction Welcome to the Data Literacy Fundamentals course! Click the link to the introductory topic below to get started.
- [Data Literacy Level 2 Post-Course Knowledge Check](https://dataliteracy.com/courses/data-literacy-level-2-post-course-knowledge-check/lessons/data-literacy-level-2-post-course-knowledge-check/)
- [Data Literacy Level 2 Pre-Course Knowledge Check](https://dataliteracy.com/courses/data-literacy-level-2-pre-course-knowledge-check/lessons/data-literacy-level-2-pre-course-knowledge-check/)
- [Part 4 - Behaviors](https://dataliteracy.com/courses/17-key-traits-of-data-literacy/lessons/part-4-behaviors/) - The Behaviors Category Our behaviors are the ways in which we act or conduct ourselves in the world. This is the final category of data literacy traits because our actions are the outcome of our knowledge, skills and attitudes, and how we ultimately make a difference with data. The other three categories don’t amount to
- [Part 3 - Attitudes](https://dataliteracy.com/courses/17-key-traits-of-data-literacy/lessons/part-3-attitudes/) - The Attitudes Category Attitudes are ways of thinking or feeling that often affect how we behave. Our attitudes stem from our knowledge and skills, and are also shaped by our interactions with others. It’s possible to know a great deal about data and build many powerful skills, and yet to be held back by unhelpful
- [Part 2 - Skills](https://dataliteracy.com/courses/17-key-traits-of-data-literacy/lessons/part-2-skills/) - The Skills Category Simply defined, skills are the abilities we possess to do something well. Data literacy doesn’t just involve knowledge about concepts and principles related to data, it also involves the ability to perform tasks and activities that uncover and convey meaning in data. It’s the second group of data literacy traits because it
- [Introduction to 17 Key Traits of Data Literacy](https://dataliteracy.com/courses/17-key-traits-of-data-literacy/lessons/17-key-traits-introduction/) - Welcome to the 17 Key Traits of Data Literacy Course and Self-Assessment! Growing in data literacy is one of the most important and strategic development paths we can embark upon. On a daily basis, our world presents us with data in many forms - numerical or graphical, raw or refined, static or dynamic. The reality
- [The EIGHT Questions to Ask Upfront](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-eight-questions-to-ask-upfront/) - Lesson 8 Overview When we come across data in any of the three domains of life—personal, public or professional—it helps to stop and ask eight important questions about the nature of the data itself and our relationship to it. These eight questions apply whether we encounter data as a single figure or statistic, as a
- [The SEVEN Groups of Data Activities](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-seven-groups-of-data-activities/) - Lesson 7 Overview When teams of people work together to turn data into wisdom, they need to perform different tasks or activities throughout the process. These tasks aren’t necessarily dedicated to any one job or role, and they don’t necessarily happen in order in a linear fashion. In large organizations, each of the different activities
- [The SIX Ways of Displaying Data](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-six-ways-of-displaying-data/) - Lesson 6 Overview When we use our sense of sight to interact with data, we notice different aspects of the data depending on how it is displayed. In this sixth lesson of the course, we'll consider six different ways that data is displayed: figures, tables, statistics, visualizations, dashboards and data stories. Each of these six
- [The FIVE Forms of Data Analysis](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-five-forms-of-data-analysis/) - Lesson 5 Overview Collecting the categorical and quantitative values we covered in the previous lesson isn't an end in itself, but rather a means to an end. The end, as we discussed in our first lesson, is to acquire knowledge and grow in wisdom. In order to mature in this way, we typically need to
- [The FOUR Types of Data Scales](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-four-types-of-data-scales/) - Lesson 4 Overview There are many ways we categorize and quantify the world around us, from groupings to ratings to counts to actual physical measurements like temperature and weight. In this fourth lesson we shift from the mindset-focused topics of the first three lessons to an intensely practical and granular study of the different scale
- [The THREE Domains of Application](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-three-domains-of-application/) - Lesson 3 Overview Most people think about data in the context of their professional pursuits, and for good reason. In this third lesson, we'll consider the emergence of data literacy as a requirement for jobs in virtually every industry and discipline. In this sense data is the key component of yet another industrial revolution that
- [The TWO Systems of Thinking](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-two-systems-of-thinking/) - Lesson 2 Overview Congratulations on finishing the first lesson of the course! Now, on to lesson 2. In this second lesson, we'll think about thinking. More specifically we'll look closely at two different systems of thought - intuition and analytics. We'll debunk a common myth that many have bought into that analytics replaces intuition. In
- [The ONE Overall Goal of Data](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-one-overall-goal-of-data/) - Lesson 1 Overview Welcome to the first lesson of the Data Literacy Fundamentals course! In this first lesson, we'll "begin with the end in mind" by considering how we can use data to ultimately grow in wisdom. Data all by itself can't help us. We need to learn how to convert it into more meaningful
- [8. Reading & Interpreting Maps](https://dataliteracy.com/courses/data-literacy-level-1/lessons/spacial-and-geospacial/) - Lesson 8 Overview In this eighth and final lesson of the course, we consider ways to see the location and movement in our data. We start with a broad overview of maps and the way map projections show a three dimensional object on a flat surface, distorting areas, angles, or some combination of the two.
- [7. Correlating Variables](https://dataliteracy.com/courses/data-literacy-level-1/lessons/correlation/) - Common Chart Types: The Scatter Plot Lesson 7 Overview In this seventh lesson of the course, we consider ways to visually compare two quantitative variables to each other. Often we are seeking to understand whether there are any underlying relationships in our data, and there is no better way than using the combination of the
- [6. Understanding Variation](https://dataliteracy.com/courses/data-literacy-level-1/lessons/distribution-and-variation/) - Common Chart Types: The Histogram Lesson 6 Overview In this sixth lesson of the course, we consider ways to understand the shape, or distribution, of quantities in our data. The values we measure, count, record and collect are rich in variety, and it helps to learn ways to see that variety clearly and to be
- [5. Seeing Change Over Time](https://dataliteracy.com/courses/data-literacy-level-1/lessons/change-over-time/) - Common Chart Types: The Line Chart Lesson 5 Overview In this fifth lesson of "Learning to See Data", we cover the all-important topic of change over time. Being able to clearly see where our data has been and where it is today is key to anticipating what might happen in the future. We start by
- [4. Relating Part-to-Whole](https://dataliteracy.com/courses/data-literacy-level-1/lessons/part-to-whole/) - Common Chart Types: The Pie Chart Lesson 4 Overview In this fourth lesson of "Learning to See Data", we cover perhaps the most controversial chart type - the pie chart - along with other encodings and chart types that express a part-to-whole relationship in the data. Often our data can be grouped into category levels
- [3. Comparing Quantities](https://dataliteracy.com/courses/data-literacy-level-1/lessons/comparing-quantities/) - Common Chart Types: the Bar Chart Lesson 3 Overview In this third lesson of "Learning to See Data", we cover perhaps the single most common and important chart type - the bar chart. We see how the bar chart encodes quantities using position, length, and even area, and we also consider ways that bar charts
- [2. Enhancing Data Tables](https://dataliteracy.com/courses/data-literacy-level-1/lessons/figures-and-tables/) - Lesson 2 Overview In this second lesson of "Learning to See Data", we start with single, solitary data points. From there, we expand to simple lists of quantities. From lists, we progress to tables of data in various formats such as unstacked, pivoted, and summary tables. Finally, we use see how visual encoding channels from
- [1. Principles of Visual Data](https://dataliteracy.com/courses/data-literacy-level-1/lessons/principles-of-visual-cognition/) - Munzner’s summary diagram of the effectiveness of encoding channels Visualization Analysis and Design. Tamara Munzner, with illustrations by Eamonn Maguire. A K Peters Visualization Series, CRC Press, 2014 Lesson 1 Overview Welcome to the first lesson of "Learning to See Data", the Data Literacy Level 1 course! In this lesson, we'll set ourselves up for
- [Data Literacy Level 1 Pre-Course Knowledge Check](https://dataliteracy.com/courses/data-literacy-level-1-pre-course-knowledge-check/lessons/data-literacy-level-1-pre-course-knowledge-check/)
- [Data Literacy Fundamentals Pre-Course Knowledge Check](https://dataliteracy.com/courses/data-literacy-fundamentals-pretest/lessons/data-literacy-fundamentals-pre-course-knowledge-check/)
- [Data Citizen Assessment](https://dataliteracy.com/lessons/data-citizen-assessment/)
- [Level 1 Placement Assessment - NBB](https://dataliteracy.com/courses/placement-assessment-nbb/lessons/level-1-placement-assessment-nbb/)
- [Fundamentals Placement Assessment - NBB](https://dataliteracy.com/courses/placement-assessment-nbb/lessons/fundamentals-placement-assessment-nbb/)
- [Fundamentals Placement Quiz](https://dataliteracy.com/courses/data-literacy-placement-assessment/lessons/fundamentals-placement-quiz/)
- [Level 1 Placement Quiz](https://dataliteracy.com/courses/data-literacy-placement-assessment/lessons/level-1-placement-quiz/)
- [Test](https://dataliteracy.com/lessons/test/)
- [Get Certificate](https://dataliteracy.com/lessons/get-certificate/) - One short lesson to get certificate for testing purposes

## Topics

- [Mix different mediums and experiences with the “Tango” prompt](https://dataliteracy.com/courses/chart-spark/lessons/communicate/topic/mix-different-mediums-and-experiences-with-the-tango-prompt/) - ⚡Summary A creative idea doesn’t have to be completely new; it can be a remix of existing ideas. This takes the pressure off of coming up with a creative idea. The more elements you bring into your work, the more unique it’ll be! ⚡Try the “Tango” prompt Turn to page 22 of your Spark Journal.
- [Explain it using a visual metaphor with the “Haystack” prompt](https://dataliteracy.com/courses/chart-spark/lessons/communicate/topic/explain-it-using-a-visual-metaphor-with-the-haystack-prompt/) - ⚡Summary Visual metaphors help our reader understand complex topics by leaning on information they already know. They also add another layer of emotion and memorability. Reach for a visual metaphor if your client is concerned that their concept is too complicated for their reader, it’s lacking emotion, or it’s important for the reader to remember
- [Find an appropriate balance with the “4Q” prompt](https://dataliteracy.com/courses/chart-spark/lessons/communicate/topic/find-an-appropriate-balance-with-the-4q-prompt/) - ⚡Summary Working with clients and stakeholders is tough because you both enter the project with different expectations and goals. It’s your job to make sure you’re both pointing at the same target and setting expectations for how “new and creative” the solution is. Ask questions to assess whether this is a good situation to just
- [Blast through project paralysis with the “Idea Isosceles” prompt](https://dataliteracy.com/courses/chart-spark/lessons/coax/topic/blast-through-project-paralysis-with-the-idea-isosceles-prompt/) - ⚡Summary Wait for the perfect idea and you’ll run out of road, like I would when merging on the highway. You need the courage to begin, the faith that an idea will come, and then over time, your confidence in ideation will grow. Use the Idea Isosceles to gain momentum and confidence in your ideation.
- [Immediately see through a new lens with the “Break-the-Box” prompt](https://dataliteracy.com/courses/chart-spark/lessons/coax/topic/immediately-see-through-a-new-lens-with-the-break-the-box-prompt/) - ⚡Summary During a creative project, often the biggest roadblock to coming up with an idea is the assumptions we unknowingly hold on to. You can see through a new lens by breaking the project into smaller parts and considering opposites. Next time you feel stuck in a project because it feels too big or it’s
- [Find stories like an editor with the “CTR” prompt](https://dataliteracy.com/courses/chart-spark/lessons/coax/topic/find-stories-like-an-editor-with-the-ctr-prompt/) - ⚡Summary Data is useless without the human lens to make meaning of it. Editors make their living creating stories that people are interested in reading, so let’s learn from them! Start with your observation in the data, then identify the conflict, timeliness, and possible resolutions to uncover more meaning in your data. You may just
- [Expand your mental boundaries with the “Bad Gifts” prompt](https://dataliteracy.com/courses/chart-spark/lessons/care/topic/expand-your-mental-boundaries-with-the-bad-gifts-prompt/) - ⚡Summary Here’s what we learned in this lesson: Your idea can’t flourish if you snip the bud. Practice being more open to the ideas you’re already having. Openness, positivity, and conscientiousness are key ingredients to creativity, as shown in this creativity flow: ⚡Try the "Bad Gifts" Prompt Turn to page 6 of your Spark Journal.
- [Build your habits with the “Recess List” prompt](https://dataliteracy.com/courses/chart-spark/lessons/care/topic/build-your-habits-with-the-recess-list-prompt/) - ⚡Summary Our creativity has seasons. Sometimes you’re in a creative winter, but you’re still a professional and expected to come up with ideas. Use habits and rituals to build your confidence even in uncertain times, just like athletes do. It’ll take experimentation and adaptation to find what works for you. Use other people’s habits as
- [Cultivate your inspiration with the “X-RAY” prompt](https://dataliteracy.com/courses/chart-spark/lessons/care/topic/cultivate-your-inspiration-with-the-x-ray-prompt/) - ⚡Summary Don’t wait around hoping to be inspired. Go out and actively pursue it. But it’s also not enough just to collect inspiration. You need to analyze it, and make sure you’re getting a balanced inspiration diet. Practical Inspiration: A spark when you see clever problem-solving in our field. X-RAY it! Internal Inspiration: A spark
- [What is creativity and why should you care?](https://dataliteracy.com/courses/chart-spark/lessons/introduction-2/topic/what-is-creativity-and-why-should-you-care/) - Watch this video, and then expand the Summary, Prompt, and More Context & Examples sections below it. Here's a handy link to the Spark Journal. ⚡Summary You don’t need to identify as a “creative type” to have creative ideas. You need to open your hands and start working. Creativity is the ability to generate new
- [3.3 The Bar Chart](https://dataliteracy.com/courses/data-literacy-level-1/lessons/comparing-quantities/topic/3-3-the-bar-chart/) - Topic Summary In topic 3 of this lesson, we'll take a slight stylistic shift away from the dot plot to the bar chart, one of the most commonly used chart types today. We will identify the different types of charts that can be used to display the same information; dot plot, bar chart and the
- [2.1 Exploring the Contours of Your Data](https://dataliteracy.com/courses/data-literacy-level-2/lessons/2-s-the-shape-phase/topic/2-1-exploring-the-contours-of-your-data/) - Topic Summary Before you analyze your data or use it to answer any questions, it's important to give it a good "once over" to see what's there; to size it up. Some call this data profiling. In this course, we call it "exploring the contours of your data." If you don't do so, you're in
- [7.2 Preparing & Analyzing Data](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-seven-groups-of-data-activities/topic/7-2-preparing-analyzing-data/) - Topic Summary In this topic, we'll continue on with the seven different groups of activities that people carry out when they work with data: preparing data and analyzing data. We'll cover the 4 steps involved in preparing data: 1) finding the data we need, 2) cleaning the data, 3) restructuring the data and 4) combining
- [5.2 Inferential](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-five-forms-of-data-analysis/topic/5-2-inferential/) - Topic Summary In this second topic of Lesson 5, we’ll consider what happens when we only have data from a subset of the total group of values. In such instances, we turn to the branch of inferential statistics to draw conclusions about the big picture - called the population - from the smaller amount of
- [5.1 Descriptive](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-five-forms-of-data-analysis/topic/5-1-descriptive/) - Topic Summary Now that we have learned how to categorize and quantify data in Lesson 4, we need to know what to "do" with all of the data we collect. In Lesson 5 we'll learn about the five different forms of data analysis: descriptive, inferential, diagnostic, predictive and prescriptive. In this topic, we'll dive into
- [4.5 The Ratio Scale](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-four-types-of-data-scales/topic/4-5-the-ratio-scale/) - Topic Summary In this fifth topic of the fourth lesson, we cover ratio scale variables, the final scale type in Stevens's set. After defining this second of the two quantitative scale types, we explore how we can use its values to make powerful comparisons like dividing to create ratios, and calculating percent change or percent
- [4.6 Objections to the 4 Scale Types](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-four-types-of-data-scales/topic/4-6-objections-to-the-4-scale-types/) - Topic Summary In this final topic of Lesson 4, we briefly cover some of the main objections to Stevens's 4-scale typology, and we introduce other characteristics of data scale types that aren't mentioned in the NOIR framework. Key Points to Remember The type of scale we apply to a variable can depend on the question
- [1.2 The Data Level of the DIKW Pyramid](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-one-overall-goal-of-data/topic/the-data-level-of-the-dikw-pyramid/) - Topic Summary In this topic, we define what "data" means, and we discuss how it forms the foundation of the DIKW Pyramid. We explore two passages from well-known texts to stress the importance of the quality of our data, which we compare to 1) the crime scene observations of a detective (Sherlock Holmes), and 2)
- [5.3 Diagnostic](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-five-forms-of-data-analysis/topic/5-3-diagnostic/) - Topic Summary In this topic, we'll use diagnostic data analysis to seek the answer to the question "what's going on under the surface?". This is accomplished by uncovering unknown or hidden factors that lie beneath the surface of our data. This involves many approaches including, 1) Drilling Down, 2) Finding Correlations and 3) Spotting Outliers.
- [3.1 Analyzing Your Data](https://dataliteracy.com/courses/data-literacy-level-2/lessons/3-d-the-discover-phase/topic/3-1-analyzing-your-data/) - Topic Summary Welcome to the first step of the DISCOVER Phase. The time has come to analyze your data! In a sense, you have already been analyzing it: you've already assessed its relevance, explored its contours, and considered some of its shortcomings. But those are just the precursors of data analysis. Data analysis is all
- [1.3 The Information Level of the DIKW Pyramid](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-one-overall-goal-of-data/topic/the-information-level-of-the-dikw-pyramid/) - Topic Summary In this topic we explore the second level of the DIKW Pyramid: Information. We consider how there can be more than one way to interpret even a simple data point, and that we need to know the meaning of the data in order to correctly understand what it's telling us. We then consider
- [6.2 The Histogram](https://dataliteracy.com/courses/data-literacy-level-1/lessons/distribution-and-variation/topic/6-2-the-histogram/) - Topic Summary In this second Topic of Lesson 6, we take the measures of central tendency to the next level and learn how to actually see how quantities are distributed. Statistical measures alone won't give a clear picture of the shape of the data, and here we'll discover a chart type that allows for this:
- [7.1 Creating Data & Building Data Sources](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-seven-groups-of-data-activities/topic/7-1-creating-data-building-data-sources/) - Topic Summary In this lesson, we’ll explore seven different groups of activities that people carry out when they work with data: creating data, building data sources, preparing data, analyzing data, presenting data, consuming data, and making data-informed decisions. In this first topic of the lesson, we’ll cover the first two activity groups - creating data
- [4.3 Hierarchies & the Treemap](https://dataliteracy.com/courses/data-literacy-level-1/lessons/part-to-whole/topic/4-3-hierarchies-the-treemap/) - Topic Summary In this final Topic of Lesson 4, we'll discover how we can use rectangular segments with varying lengths and widths (unlike a traditional bar chart where the widths are always equal and only the lengths vary). The chart type we'll consider converts hierarchical data into rectangles that are grouped together to form a
- [6.1 Figures, Tables & Statistics](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-six-ways-of-displaying-data/topic/6-1-figures-tables-statistics/) - Topic Summary In this 6th lesson of the course, we’ll consider 6 different ways of displaying data, each with its own valid use: figures, tables, statistics, visualizations, dashboards and data stories. For Topic 1, we'll focus on the first three, figures, tables and statistics and how these displays impact our ability to read data. Key
- [3.2a Determining the Significance of Your Findings: Significance, and the Chi-Squared Test](https://dataliteracy.com/courses/data-literacy-level-2/lessons/3-d-the-discover-phase/topic/3-2a-determining-the-significance-of-your-findings-significance-and-the-chi-squared-test/) - Topic Summary When your analysis is complete, it's critical to assess both the practical significance and the statistical significance of your findings. It's possible that what you've found doesn't make a difference in the real world (that is, it's not practically significant). And it's also possible that what you've found could be due to nothing
- [6.3 Data Dashboards](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-six-ways-of-displaying-data/topic/6-3-data-dashboards/) - Topic Summary In the first two topics of this lesson, we covered four of the six displays of data: figures, tables, statistics and visualizations. In this third topic, we cover the fifth display type: data dashboards. Using data dashboards helps us to describe views of data that combine elements such as figures, statistics, tables and
- [6.2 Data Visualizations](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-six-ways-of-displaying-data/topic/6-2-data-visualizations/) - Topic Summary Continuing on from the first topic in Lesson 6, we'll focus on the 4th display type, visualizations. When we visualize data, we use different types of encodings to turn quantitative and categorical data values into graphical attributes that combine together to form the charts and graphs we see everyday. In this topic, we'll
- [1. Basic Elements of Data](https://dataliteracy.com/courses/17-key-traits-of-data-literacy/lessons/part-1-knowledge/topic/1-basic-elements-of-data/) - Trait #1 - Basic Elements of Data How to Score Yourself As you fill out your self-assessment worksheet or dashboard, take into account the following statements to more accurately gauge your own level of proficiency in this trait, as well as its importance to your success in the near future. Proficiency Rate yourself high in
- [9. Communicate Data Effectively](https://dataliteracy.com/courses/17-key-traits-of-data-literacy/lessons/part-2-skills/topic/9-communicate-data-effectively/) - Trait #9 - Communicate Data Effectively How to Score Yourself As you fill out your self-assessment worksheet or dashboard, take into account the following statements to more accurately gauge your own level of proficiency in this trait, as well as its importance to your success in the near future: Proficiency Rate yourself high in proficiency
- [8. Create Clear Visuals](https://dataliteracy.com/courses/17-key-traits-of-data-literacy/lessons/part-2-skills/topic/8-create-clear-visuals/) - Trait #8 - Create Clear Visuals How to Score Yourself As you fill out your self-assessment worksheet or dashboard, take into account the following statements to more accurately gauge your own level of proficiency in this trait, as well as its importance to your success in the near future: Proficiency Rate yourself high in proficiency
- [7. Explore Data](https://dataliteracy.com/courses/17-key-traits-of-data-literacy/lessons/part-2-skills/topic/7-explore-data/) - Trait #7 - Explore Data How to Score Yourself As you fill out your self-assessment worksheet or dashboard, take into account the following statements to more accurately gauge your own level of proficiency in this trait, as well as its importance to your success in the near future: Proficiency Rate yourself high in proficiency in
- [3.2b Determining the Significance of Your Findings: The t-test, ANOVA, and Regression](https://dataliteracy.com/courses/data-literacy-level-2/lessons/3-d-the-discover-phase/topic/3-2b-determining-the-significance-of-your-findings-the-t-test-anova-and-regression/) - Topic Summary When assessing the practical and statistical significance of differences between the quantitative variables taken from sample data, you'll want to consider using one of three common Null Hypothesis Statistical Tests (NHST) to make sure you're not making unreasonable inferences about the broader population. You'll use either Student's t-test or ANOVA if you're comparing
- [2.3 Cleaning & Structuring Your Data](https://dataliteracy.com/courses/data-literacy-level-2/lessons/2-s-the-shape-phase/topic/2-3-cleaning-structuring-your-data/) - Topic Summary After profiling your data by exploring its contours and then contemplating its shortcomings, you now have to ask yourself whether or not it’s ready for you to begin analyzing it to get answers to your most important question, and to put your guess or your hypothesis to the test. But how do you
- [1.4 Finding, Gathering, or Creating Data](https://dataliteracy.com/courses/data-literacy-level-2/lessons/1-w-the-wonder-phase/topic/1-4-finding-gathering-or-creating-data/) - Topic Summary The next step in the process of converting data into wisdom is to ask whether you have the data you need in order to answer your question and put your hypothesis to the test. Sometimes relevant data has already been turned into a chart or dashboard for you by someone else. Oftentimes though, there’s
- [Fundamentals Course Introduction](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/fundamentals-course-introduction/topic/fundamentals-course-introduction/) - Course Introduction Welcome to the Data Literacy Fundamentals course! This course is designed to take you through eight different lessons that teach you the power and value of data. There are no prerequisites for the course, and learners of any level can dive right in. If you're relatively new to data, or even somewhat "dataphobic",
- [4.3 The Ordinal Scale](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-four-types-of-data-scales/topic/4-3-the-ordinal-scale/) - Topic Summary This topic covers the second categorical data type - ordinal scales. Ordinal scales are similar to nominal scales in that they capture different categories of things, but they add the attribute of inherent order. Levels in an ordinal scale are either less than or greater than other levels in the same scale. Key
- [4.3 Enacting Appropriate Changes or Decisions](https://dataliteracy.com/courses/data-literacy-level-2/lessons/4-m-the-mature-phase/topic/4-3-enacting-appropriate-changes-or-decisions/) - Topic Summary Guess what! You’ve made it to the end of the WISDOM Data Working Flow! You’re at the last decision step and the last process step of the entire framework. At this point in the MATURE Phase, you’ve just taken in your audience's feedback. So ask yourself: did any new questions arise that change things?
- [4.2 Listening to Your Audience's Feedback](https://dataliteracy.com/courses/data-literacy-level-2/lessons/4-m-the-mature-phase/topic/4-2-listening-to-your-audiences-feedback/) - Topic Summary A huge part of communicating well involves gathering feedback. In this second step of the MATURE Phase, you'll learn six ways to collect feedback from your audience in order to really nail this part of the process. You’re looking to make an impact on your intended audience, and you can’t expect to do
- [4.1 Crafting & Delivering a Notable Message](https://dataliteracy.com/courses/data-literacy-level-2/lessons/4-m-the-mature-phase/topic/4-1-crafting-delivering-a-notable-message/) - Topic Summary In this first step of the MATURE Phase, we’ll talk about how to craft and deliver a notable message with data. For a presentation to be notable, it has to cut through the clutter and noise of the busy, everyday lives of your audience members. To do so, it has to be remarkable
- [3.3 Getting More Data, Fixing Issues & Re-Analyzing](https://dataliteracy.com/courses/data-literacy-level-2/lessons/3-d-the-discover-phase/topic/3-3-getting-more-data-fixing-issues-re-analyzing/) - Topic Summary The WISDOM Data Working Flow is rarely a perfectly linear process. If you do it right, you'll loop back, redo, rehash, and tweak your approach multiple times. Rework here is not a defect or a problem, it's the way it works. At this point in the process, you've conducted your analysis, and you've
- [2.2 Considering the Shortcomings of Your Data](https://dataliteracy.com/courses/data-literacy-level-2/lessons/2-s-the-shape-phase/topic/2-2-considering-the-shortcomings-of-your-data/) - Topic Summary Now that you've spent some time obtaining a profile of your data, you're in a position to assess its virtues as well as its shortcomings. No data set is entirely free of shortcomings. Most data sets suffer from more than one. This topic introduces you to nine of the most common types of
- [1.2 Asking Worthwhile Questions](https://dataliteracy.com/courses/data-literacy-level-2/lessons/1-w-the-wonder-phase/topic/1-2-asking-worthwhile-questions/) - Topic Summary The next step in the process of converting data into wisdom is to ask worthwhile questions. In some ways our questions are even more important than the answers we get. They define the scope of our thinking, and they set the stage for our learning opportunities. In this second step of the WISDOM
- [1.1 Making Keen Observations](https://dataliteracy.com/courses/data-literacy-level-2/lessons/1-w-the-wonder-phase/topic/1-1-making-keen-observations/) - Topic Summary Before you dive into your data, or even think about how to use it to answer your questions, it helps to stop and consider the way you observe the world around you. A couple points become clear: first, that you have amazing powers of observation. You take in information using your five senses,
- [1.3 Forming a Falsifiable Hypothesis](https://dataliteracy.com/courses/data-literacy-level-2/lessons/1-w-the-wonder-phase/topic/1-3-forming-a-falsifiable-hypothesis/) - Topic Summary Once you've posed a worthwhile question, the next step is to make a guess about what the answer might be. Such a guess is often called a hypothesis. A hypothesis is a tentative guess – hopefully an educated one – about the explanation behind something. It leads to a prediction about what you might
- [8.2 Questions 5-8](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-eight-questions-to-ask-upfront/topic/8-2-questions-5-8/) - Topic Summary In this final lesson of Lesson 8 and of the whole course, we'll cover the second half of the eight most important questions to ask upfront, questions 5 through 8: Which are the most important variables? What are the definitions of the important variables? How was the data measured, collected, and stored? How
- [8.1 Questions 1-4](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-eight-questions-to-ask-upfront/topic/8-1-the-8-questions-to-ask-upfront/) - Topic Summary In Lesson 8 we cover the eight important questions about the nature of the data itself and our relationship to it. In topic 1, we'll cover the first four questions: Why does the data matter to you? Where did the data come from? Who owns and updates the data? When was the data
- [7.3 Presenting Data, Consuming Data & Making Data-Informed Decisions](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-seven-groups-of-data-activities/topic/7-3-presenting-data-consuming-data-making-data-informed-decisions/) - Topic Summary In this final topic of Lesson 7, we'll cover the final three of the seven different groups of activities that people carry out when they work with data: presenting data, consuming data presentations, and making data-informed decisions. Key Points to Remember Creating and delivering notable data presentations is a unique skill as compared
- [6.4 Data Stories](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-six-ways-of-displaying-data/topic/6-4-data-stories/) - Topic Summary In this final topic of Lesson 6 we’ll cover the 6th display type, data stories. Data storytelling involves sequencing individual data insights to form a cohesive narrative in order to give the audience a deeper understanding of the data. Each of the six ways of displaying data have their own uses, and they
- [5.4 Predictive and Prescriptive](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-five-forms-of-data-analysis/topic/5-4-predictive-and-prescriptive/) - Topic Summary In this final topic of our 5th lesson, we’ll consider how we can use data to think about the future, and how we can plan a course of action. Predictive and Prescriptive Analytics are our 4th and 5th types of data analysis, and they both involve using historical data to make predictions about
- [3.4 The Personal Domain](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-three-domains-of-application/topic/3-4-the-personal-domain/) - Topic Summary Data isn't only helpful for groups of people such as organizations or towns, it can also be incredibly powerful for each individual seeking to develop and improve their own life. This topic introduces the usefulness of data in the personal domain. Key Points to Remember There's a long history of people collecting and
- [3.3 The Public Domain](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-three-domains-of-application/topic/3-3-the-public-domain/) - Topic Summary Data can also be used as a powerful force for good in the public domain. We can use it to shine the spotlight on major issues that affect the entire world like climate change or pandemics, and we can use it to help organize and run our countries and our communities. Just because
- [3.1 Introduction to the Three Domains](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-three-domains-of-application/topic/3-1-introduction-to-the-three-domains/) - Topic Summary In this video we introduce the concept of the 3 P's of data application - Professional, Public & Personal. These three domains represent areas of life in which we can apply data to grow in wisdom. Key Points to Remember The data skills we develop aren't only for our employers, they're also for
- [6.3 The Box Plot](https://dataliteracy.com/courses/data-literacy-level-1/lessons/distribution-and-variation/topic/6-3-the-box-plot/) - Topic Summary There’s another type of chart that gives us valuable information about the distribution of a set of numbers. It’s called the box plot, or sometimes the box-and-whisker plot and this will be the final chart type we cover in Lesson 6. While the box plot is very popular in academic research and in
- [8.4 The Choropleth Map](https://dataliteracy.com/courses/data-literacy-level-1/lessons/spacial-and-geospacial/topic/8-4-the-choropleth-map/) - Topic Summary In this last Topic of this Lesson and the course, we'll learn about one more map type, the choropleth map. Choropleth maps allow us to move from 1D marks (lines) to 2D marks (filled regions or areas). Generally speaking, a choropleth map defines zones that are separated by borders or boundaries from other
- [8.3 The Flow Map](https://dataliteracy.com/courses/data-literacy-level-1/lessons/spacial-and-geospacial/topic/8-3-the-flow-map/) - Topic Summary In this Topic, we'll move from maps with points to maps with lines. Actually, we have already considered lines on maps: the projections with their parallels of constant latitude and meridians of constant longitude. We'll add to that the topographic map with contour lines of constant elevation. And then we'll consider flow maps
- [8.2 The Symbol Map](https://dataliteracy.com/courses/data-literacy-level-1/lessons/spacial-and-geospacial/topic/8-2-the-symbol-map/) - Topic Summary In this second Topic of Lesson 8, we'll learn about the value of the symbol map and how it allows us to see locations in our data. We learn how symbol maps, like scatter plots, make use of the highly effective position encoding channel twice to plot our data. Symbol maps are essentially
- [8.1 A Brief Primer on Maps](https://dataliteracy.com/courses/data-literacy-level-1/lessons/spacial-and-geospacial/topic/8-1-a-brief-primer-on-maps/) - Topic Summary In this introductory Topic of the final Lesson in this course, we introduce you to maps and a brief history of how maps were created and why they are important to us today. We'll also consider the concept of a map projection, or, how we portray a three dimensional globe on a two
- [7.4 Correlations Over Time](https://dataliteracy.com/courses/data-literacy-level-1/lessons/correlation/topic/7-4-correlations-over-time/) - Topic Summary In this final Topic of Lesson 7, we'll talk about the chart types that are most effective for learning about how correlations change over time. While we can simply place two separate charts side-by-side for comparison, it can be hard to view the data effectively or to see what happened in the time
- [7.3 The Bubble Chart](https://dataliteracy.com/courses/data-literacy-level-1/lessons/correlation/topic/7-3-the-bubble-chart/) - Topic Summary So far in this Lesson, we've discovered how scatter plots can help us encode the relationships between two quantitative variables which display circles of the exact same size. In this third Topic of Lesson 7 we'll discover how we can create a Bubble Chart by adding a third variable which encodes the area
- [7.2 Regression](https://dataliteracy.com/courses/data-literacy-level-1/lessons/correlation/topic/7-2-regression/) - Topic Summary In this second Topic of Lesson 7, we'll further explore correlation through understanding the concept of regression and how it helps us determine the type of relationship we see between the two variables in a scatter plot as well as how strong it is. We'll learn about trend lines or "best-fit lines" and
- [7.1 The Scatter Plot](https://dataliteracy.com/courses/data-literacy-level-1/lessons/correlation/topic/7-1-the-scatter-plot/) - Topic Summary In Lesson 7, we’ll learn how to see the nature of the relationship between two or more quantitative variables. We'll also look closely at correlation, starting with the visual aspect. We’ll consider correlations that are almost certainly causal (changes in one variable directly cause the other variable to change), those that are spurious
- [6.1 Central Tendency & Dispersion](https://dataliteracy.com/courses/data-literacy-level-1/lessons/distribution-and-variation/topic/6-1-central-tendency-dispersion/) - Topic Summary In Lesson 6 we will begin to understand variation and how to chart, encode and visualize quantitative measures that vary, as most do. We’ve already looked closely at how to compare quantities for different categorical variables, like population by region, but we have yet to consider how a single quantitative variable - any
- [5.1 Mapping Time to Space](https://dataliteracy.com/courses/data-literacy-level-1/lessons/change-over-time/topic/5-1-mapping-time-to-space/) - Topic Summary In this first Topic of Lesson 5, we will uncover the mappings of time to space and how the language we speak and where we are from can impact how we map time to space in the visual context. We'll come to the understanding that where things are today needs to be accompanied
- [5.4 The Area Chart](https://dataliteracy.com/courses/data-literacy-level-1/lessons/change-over-time/topic/5-4-the-area-chart/) - Topic Summary In this final Topic of Lesson 5 we review the area chart, a chart type that moves from 1D lines to 2D areas by filling in the gap between the line and the horizontal axis. We'll gain an understanding of when an area chart is best used, and what pitfalls to be on
- [5.3 Multiple Lines](https://dataliteracy.com/courses/data-literacy-level-1/lessons/change-over-time/topic/5-3-multiple-lines/) - Topic Summary In Topic 3 we will learn about multiple lines and how, just like with bar charts, we sometimes want to see different levels of a categorical variable in a line chart. When this happens, multiple lines become necessary. We'll also discover how line charts work well to show percent change when comparing relative
- [5.2 The Line Chart](https://dataliteracy.com/courses/data-literacy-level-1/lessons/change-over-time/topic/5-2-the-line-chart/) - Topic Summary In Topic 2, we begin by discovering that lines are typically more expressive of change over time than bars . We then discuss a common convention for the vertical axis: "up" is often connected with "more" or "better", and "down" with "less" or "worse". We then review different ways to draw lines to
- [4.2 The Pie Chart & the Donut](https://dataliteracy.com/courses/data-literacy-level-1/lessons/part-to-whole/topic/4-2-the-pie-chart-and-the-donut/) - Topic Summary In the second Topic of Lesson 4, we'll learn about the pie chart, and why it's the most controversial chart type, by far. The pie chart is simply a stacked bar chart created using polar instead of rectangular coordinates. Instead of dividing a bar into linear segments, we divide a circle in sectors,
- [4.1 The Stacked Bar & the Waterfall](https://dataliteracy.com/courses/data-literacy-level-1/lessons/part-to-whole/topic/4-1-the-stacked-bar-and-the-waterfall/) - Topic Summary In Topic 1 of Lesson 4, we learn effective ways to see part-to-whole relationships in our data. We start by considering common part-to-whole scenarios, such as putting together components to form an assembly, or breaking down a company's budget into different departments. Once again, we consider historical evidence that these sorts of considerations
- [3.4 The 2D Bar Chart Grid](https://dataliteracy.com/courses/data-literacy-level-1/lessons/comparing-quantities/topic/3-4-the-2d-bar-chart-grid/) - Topic Summary In Lesson 3, we have learned so far that there are many ways to visualize and orient marks when we want to compare quantities. In this final Topic of the lesson, we will learn how we do this when considering more than one category at a time. In order to evaluate the suitability
- [3.2 The Dot Plot](https://dataliteracy.com/courses/data-literacy-level-1/lessons/comparing-quantities/topic/3-2-the-dot-plot/) - Topic Summary Position is considered by many to be the most effective way of capturing and encoding relative magnitude. This is because our minds are capable of more making accurate judgments of relative position than relative area, angle, color saturation, volume and the other magnitude channels. Modern visualization research is revealing just how ingenious the
- [3.1 How Much and How Many?](https://dataliteracy.com/courses/data-literacy-level-1/lessons/comparing-quantities/topic/3-1-how-much-and-how-many/) - Topic Summary In Lesson 3, we go beyond figures & tables and we begin our exploration of visual encodings of various kinds. In this first topic of Lesson 3, we start by asking ourselves whether the data we're using is a valid way to take stock of our environment. Then we discuss how categorizing things
- [2.3 From Tables to Heatmaps and Beyond](https://dataliteracy.com/courses/data-literacy-level-1/lessons/figures-and-tables/topic/2-3-from-tables-to-heatmaps-and-beyond/) - Topic Summary In this final topic of Lesson 2, we'll consider how we can more easily spot patterns, outliers and trends in data tables when color encodes the numbers or cells of the table. We'll consider how color hue can be added to make specific values stand out, and we'll consider how color saturation can
- [2.2 Displaying Data in Tables](https://dataliteracy.com/courses/data-literacy-level-1/lessons/figures-and-tables/topic/2-2-displaying-data-in-tables/) - Topic Summary In this Topic, we consider how multiple individual data points can be brought together into tables of rows and columns - something we've been doing as a species for quite a long time, actually. From ancient clay tablets to modern databases and spreadsheet software, we've been building tables of data for at least
- [2.1 Individual Data Points](https://dataliteracy.com/courses/data-literacy-level-1/lessons/figures-and-tables/topic/2-1-individual-data-points/) - Topic Summary Solitary numbers can hold such significance for us, sometimes. Often we’re simply looking for a single number or value - a data point. A data point all by itself lacks context and comparative power, but it can often provide us with the information we need to make a decision or to get a
- [1.1 The Human Visual System](https://dataliteracy.com/courses/data-literacy-level-1/lessons/principles-of-visual-cognition/topic/1-1-the-human-visual-system/) - Topic Summary In Lesson 1, Topic 1, we will begin this course by understanding how the human visual system functions in order to help us understand its' significance in reading and interpreting data. With this understanding, we will be able to avoid common pitfalls that are easy to fall into when consuming data visualizations. Given
- [1.5 Effectiveness of Encodings](https://dataliteracy.com/courses/data-literacy-level-1/lessons/principles-of-visual-cognition/topic/1-5-effectiveness-of-encodings/) - Topic Summary As we learned in Topic 4, we have to first establish that a visual encoding follows the expressiveness principle, and shows all of, and only, the information contained in the data itself - nothing more, nothing less. In Topic 5, we'll focus on the next step which is determining whether the visual encoding
- [1.3 Graphical Marks & Encoding Channels](https://dataliteracy.com/courses/data-literacy-level-1/lessons/principles-of-visual-cognition/topic/1-3-graphical-marks-encoding-channels/) - Topic Summary In Lesson 1, Topic 2 we reminded ourselves about the different scale types that our data variables can fall into (nominal, ordinal, interval, ration). In this 3rd topic of Lesson 1, we'll consider how people use these variables to create graphical displays that can show us what the data contains. Instead of just
- [1.4 Expressiveness of Encodings](https://dataliteracy.com/courses/data-literacy-level-1/lessons/principles-of-visual-cognition/topic/1-4-expressiveness-of-encodings/) - Topic Summary In Topic 4 of Lesson 1 we will consider the first of two criteria we can use to evaluate an encoding: expressiveness. Expressiveness means that the chosen encoding expresses all of, and only, what's in the data. To better understand the concept of expressiveness, we'll consider at examples in which an encoding can
- [1.2 A Review of Data Scale Types](https://dataliteracy.com/courses/data-literacy-level-1/lessons/principles-of-visual-cognition/topic/1-2-a-review-of-data-scale-types/) - Topic Summary In Lesson 1 Topic 2, we will reintroduce the four different data scale types (nominal, ordinal, interval and ratio) proposed by Stanley Smith Stevens, a Harvard psychologist and researcher in 1946. These were reviewed in Data Literacy Fundamentals, but given their significance, we will briefly review them again here. The four scale types
- [2.4 The Dark Side of Intuition](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-two-systems-of-thinking/topic/2-4-the-dark-side-of-intuition/) - Topic Summary In the previous topic, we gave five specific ways that intuition can be used in the analytics process. But just like analytics, human intuition is imperfect and can mislead us in various ways. We are subject to many cognitive biases - blind spots and mental glitches - that can affect both numeracy and
- [1.4 The Knowledge Level of the DIKW Pyramid](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-one-overall-goal-of-data/topic/the-knowledge-level-of-the-dikw-pyramid/) - Topic Summary In this topic, we talk about the difference between information and knowledge, and we discuss how to convert information into knowledge by linking it together with other pieces of information. Simply knowing one piece of information often leaves us wondering "So what?" You may have converted your checking account data into information about
- [1.5 The Wisdom Level of the DIKW Pyramid](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-one-overall-goal-of-data/topic/the-wisdom-level-of-the-dikw-pyramid/) - Topic Summary We reach the pinnacle of the DIKW Pyramid: the Wisdom Level. This level involves applying the knowledge we've formed out of the information that our data provided to us. In this topic, we embrace that wisdom involves even more of our humanity than the levels below it. Our goals and priorities factor in,
- [1.1 An Overview of the DIKW Pyramid](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-one-overall-goal-of-data/topic/an-overview-of-the-dikw-pyramid/) - Topic Summary The DIKW Pyramid is introduced as a model we can use to understand the way data is converted to wisdom, which is the one overall goal of data. In this first topic, we consider the model at a high level, and we will consider each level in more detail in subsequent topics. Key
- [17. Enthusiastically Spreads Data Literacy](https://dataliteracy.com/courses/17-key-traits-of-data-literacy/lessons/part-4-behaviors/topic/17-enthusiastically-spreads-data-literacy/) - Trait #17 - Enthusiastically Spreads Data Literacy How to Score Yourself As you fill out your self-assessment worksheet or dashboard, take into account the following statements to more accurately gauge your own level of proficiency in this trait, as well as its importance to your success in the near future: Proficiency Rate yourself high in
- [16. Effectively Advocates for Data](https://dataliteracy.com/courses/17-key-traits-of-data-literacy/lessons/part-4-behaviors/topic/16-effectively-advocates-for-data/) - Trait #16 - Effectively Advocates for Data How to Score Yourself As you fill out your self-assessment worksheet or dashboard, take into account the following statements to more accurately gauge your own level of proficiency in this trait, as well as its importance to your success in the near future: Proficiency Rate yourself high in
- [15. Continuously Improves Data](https://dataliteracy.com/courses/17-key-traits-of-data-literacy/lessons/part-4-behaviors/topic/15-continuously-improves-data/) - Trait #15 - Continuously Improves Data How to Score Yourself As you fill out your self-assessment worksheet or dashboard, take into account the following statements to more accurately gauge your own level of proficiency in this trait, as well as its importance to your success in the near future: Proficiency Rate yourself high in proficiency
- [14. Utilizes Data Resourcefully](https://dataliteracy.com/courses/17-key-traits-of-data-literacy/lessons/part-4-behaviors/topic/14-resourcefully-utilizes-data/) - Trait #14 - Utilizes Data Resourcefully How to Score Yourself As you fill out your self-assessment worksheet or dashboard, take into account the following statements to more accurately gauge your own level of proficiency in this trait, as well as its importance to your success in the near future: Proficiency Rate yourself high in proficiency
- [13. Ethical](https://dataliteracy.com/courses/17-key-traits-of-data-literacy/lessons/part-3-attitudes/topic/13-ethical/) - Trait #13 - Ethical How to Score Yourself As you fill out your self-assessment worksheet or dashboard, take into account the following statements to more accurately gauge your own level of proficiency in this trait, as well as its importance to your success in the near future: Proficiency Rate yourself high in proficiency in this
- [12. Alert](https://dataliteracy.com/courses/17-key-traits-of-data-literacy/lessons/part-3-attitudes/topic/12-alert/) - Trait #12 - Alert How to Score Yourself As you fill out your self-assessment worksheet or dashboard, take into account the following statements to more accurately gauge your own level of proficiency in this trait, as well as its importance to your success in the near future: Proficiency Rate yourself high in proficiency in this
- [11. Confident](https://dataliteracy.com/courses/17-key-traits-of-data-literacy/lessons/part-3-attitudes/topic/11-confident/) - Trait #11 - Confident How to Score Yourself As you fill out your self-assessment worksheet or dashboard, take into account the following statements to more accurately gauge your own level of proficiency in this trait, as well as its importance to your success in the near future: Proficiency Rate yourself high in proficiency in this
- [10. Inclusive](https://dataliteracy.com/courses/17-key-traits-of-data-literacy/lessons/part-3-attitudes/topic/10-inclusive/) - Trait #10 - Inclusive How to Score Yourself As you fill out your self-assessment worksheet or dashboard, take into account the following statements to more accurately gauge your own level of proficiency in this trait, as well as its importance to your success in the near future: Proficiency Rate yourself high in proficiency in this
- [6. Prepare Data for Analysis](https://dataliteracy.com/courses/17-key-traits-of-data-literacy/lessons/part-2-skills/topic/6-prepare-data-for-analysis/) - Trait #6 - Prepare Data for Analysis How to Score Yourself As you fill out your self-assessment worksheet or dashboard, take into account the following statements to more accurately gauge your own level of proficiency in this trait, as well as its importance to your success in the near future: Proficiency Rate yourself high in
- [5. Read Visual Displays of Data](https://dataliteracy.com/courses/17-key-traits-of-data-literacy/lessons/part-2-skills/topic/5-read-visual-displays-of-data/) - Trait #5 - Read Visual Displays of Data How to Score Yourself As you fill out your self-assessment worksheet or dashboard, take into account the following statements to more accurately gauge your own level of proficiency in this trait, as well as its importance to your success in the near future (note that some of
- [4. Data Visualization Rules of Thumb](https://dataliteracy.com/courses/17-key-traits-of-data-literacy/lessons/part-1-knowledge/topic/4-data-visualization-rules-of-thumb/) - Trait #4 - Data Visualization Rules of Thumb How to Score Yourself As you fill out your self-assessment worksheet or dashboard, take into account the following statements to more accurately gauge your own level of proficiency in this trait, as well as its importance to your success in the near future: Proficiency Rate yourself high
- [3. Data Analysis Principles](https://dataliteracy.com/courses/17-key-traits-of-data-literacy/lessons/part-1-knowledge/topic/3-data-analysis-principles/) - Trait #3 - Data Analysis Principles How to Score Yourself As you fill out your self-assessment worksheet or dashboard, take into account the following statements to more accurately gauge your own level of proficiency in this trait, as well as its importance to your success in the near future: Proficiency Rate yourself high in proficiency
- [2. Data Storage Methods](https://dataliteracy.com/courses/17-key-traits-of-data-literacy/lessons/part-1-knowledge/topic/2-data-storage-methods/) - Trait #2 - Data Storage Methods How to Score Yourself As you fill out your self-assessment worksheet or dashboard, take into account the following statements to more accurately gauge your own level of proficiency in this trait, as well as its importance to your success in the near future: Proficiency Rate yourself high in proficiency
- [1.6 The Pop Out Effect](https://dataliteracy.com/courses/data-literacy-level-1/lessons/principles-of-visual-cognition/topic/1-6-the-pop-out-effect/) - Topic Summary In our final topic of lesson 1, we'll take into consideration another important aspect of human perception to consider when analyzing how our minds interact with visual encodings of data. Certain aspects of visuals tend to grab our attention. When this happens immediately and automatically, it's called "pop out". Depending on how a
- [4.4 The Interval Scale](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-four-types-of-data-scales/topic/4-4-the-interval-scale/) - Topic Summary In this topic we consider the third data scale type, the interval scales, which form the "I" of "NOIR". Interval scales are quantitative in nature, and the differences between values is meaningful. We cover a tricky attribute of interval scales - the fact that they don't have a "true" zero point (i.e., one
- [4.2 The Nominal Scale](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-four-types-of-data-scales/topic/4-2-the-nominal-scale/) - Topic Summary The first and most basic of the four scale types is the nominal scale. In this topic, we cover this scale type, and we learn what it is used for, and the kinds of values it can take. In the notes below, we consider the notion of a boolean data type, a special
- [4.1 Introduction to the Four Scale Types](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-four-types-of-data-scales/topic/4-1-introduction-to-the-four-scales-types/) - Topic Summary There are many ways to measure things in the world around us, and we can classify various measurement scales into different types that have similar properties. This topic introduces four different types of data scales – nominal, ordinal, interval, and ratio – and shows how these four types are grouped into two pairs
- [3.5 The Goal Tree](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-three-domains-of-application/topic/3-5-the-goal-tree/) - Topic Summary In this topic, we introduce the technique known as The Goal Tree. Goal Trees are helpful ways to break down a high-level goal into various smaller pieces that combine together to produce the overall affect we hope to improve. We apply this technique to simple examples in professional and personal contexts. Key Points
- [2.5 An Idea Rooted in Antiquity](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-two-systems-of-thinking/topic/2-5-an-idea-rooted-in-antiquity/) - Topic Summary In this final topic of the second lesson, we review ancient texts that reveal that the "dual process theory" notion is similar to the way humans thought about the mind centuries ago. Specifically, ancient thinkers in both the West and the East envisioned humanity as a chariot in which the charioteer and horses
- [2.3 The 5 Roles of Intuition in Analysis](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-two-systems-of-thinking/topic/2-3-the-5-roles-of-intuition-in-analysis/) - Topic Summary We've established that System 1 (intuition) and System 2 (analytics) are different cognitive processes that take place in our mind. We've further considered that they don't need to compete with one another, but that we can use them together to leverage the strengths of each. In this topic we get more specific about
- [2.2 System 1 and System 2 Thinking](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-two-systems-of-thinking/topic/2-2-system-1-and-system-2-thinking/) - Topic Summary In this second topic of Lesson 2, we dive deeper into the two modes of thinking: System 1 and System 2. Specifically, we stress that the two sides of thinking in this "dual process theory" can work together. Instead of intuition and analytics competing with one another in a horse-versus-locomotive race, we can
- [2.1 An Introduction to the Two Systems of Thinking](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-two-systems-of-thinking/topic/an-introduction-to-the-two-systems-of-thinking/) - Topic Summary As we kick off the second lesson of the course, we introduce a common misconception about data - that it empowers us to replace our flawed intuition with flawless analytics. This popular but false notion doesn't match recent research into the way our minds work. The model called "dual process theory" shows us
- [3.2 The Professional Domain](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-three-domains-of-application/topic/3-2-the-professional-domain/) - Topic Summary The way we get things done at work has changed dramatically over the past few centuries, and even during the past few decades. The Information Age has led to yet another industrial revolution in which virtually everyone needs to speak the language of data in order to contribute and thrive. In this topic,
- [Topic](https://dataliteracy.com/topic/topic-2/)
- [Topic](https://dataliteracy.com/topic/topic/)

## Quizzes

- [Lesson 5: Clarify](https://dataliteracy.com/courses/data-storytelling-for-impact/lessons/lesson-5-clarify/quizzes/lesson-5-clarify/)
- [Lesson 6: Test](https://dataliteracy.com/courses/data-storytelling-for-impact/lessons/lesson-6-test/quizzes/lesson-6-test/)
- [Data Storytelling for IMPACT Post-Course Knowledge Check](https://dataliteracy.com/courses/data-storytelling-for-impact/lessons/conclusion-to-data-storytelling-for-impact/quizzes/data-storytelling-for-impact-pre-course-knowledge-check-2/) - Now that you've completed the course, let's see how much you've learned!
- [Lesson 4: Align](https://dataliteracy.com/courses/data-storytelling-for-impact/lessons/lesson-4-align/quizzes/lesson-4-align/)
- [Lesson 3: Plan](https://dataliteracy.com/courses/data-storytelling-for-impact/lessons/lesson-3-plan/quizzes/lesson-3-plan/)
- [Level 2 Pre-Course Knowledge Check](https://dataliteracy.com/courses/data-literacy-level-2-v2/lessons/introduction-to-data-literacy-level-2-v2/quizzes/level-2-pre-course-knowledge-check/) - Welcome to the Data Literacy Level 2 pre-course knowledge check. This brief 10-question assessment is designed to gauge your initial understanding of the core concepts we’ll cover in the course. Don’t worry if you find some questions challenging, as this check is meant to highlight your learning journey, from start to finish.
- [Lesson 2: Message](https://dataliteracy.com/courses/data-storytelling-for-impact/lessons/lesson-2-message/quizzes/lesson-2-message/)
- [Lesson 1: Insight](https://dataliteracy.com/courses/data-storytelling-for-impact/lessons/lesson-1-insight/quizzes/lesson-1-insight/)
- [Data Storytelling for IMPACT Pre-Course Knowledge Check](https://dataliteracy.com/courses/data-storytelling-for-impact/lessons/introduction-to-data-storytelling-for-impact/quizzes/data-storytelling-for-impact-pre-course-knowledge-check/) - Welcome to the Data Storytelling for IMPACT pre-course knowledge check. This brief 10-question assessment is designed to gauge your initial understanding of the core concepts we’ll cover in the course. Don’t worry if you find some questions challenging, as this check is meant to highlight your learning journey, from start to finish.
- [Level 2 Post-Course Knowledge Check](https://dataliteracy.com/courses/data-literacy-level-2-v2/lessons/wrapping-up-data-literacy-level-2-v2/quizzes/level-2-post-course-knowledge-check/) - Congratulations on reaching the end of the Data Literacy Level 2 course! It’s time for a 10-question knowledge check to assess your grasp of the key concepts we’ve explored. This post-course assessment aims to showcase your progress from beginning to end.
- [Level 2 Lesson 4 Quiz](https://dataliteracy.com/courses/data-literacy-level-2-v2/lessons/level-2-lesson-4/quizzes/level-2-lesson-4-quiz/) - Let’s test your knowledge of the ideas and concepts covered in Lesson 4 of the Data Literacy Level 2 course. You will need a score of 80% or better on each of the quizzes in order to earn your Data Explorer badge. At the end of the quiz you will be given a chance to
- [Level 2 Lesson 3 Quiz](https://dataliteracy.com/courses/data-literacy-level-2-v2/lessons/level-2-lesson-3/quizzes/level-2-lesson-3-quiz/) - Let's test your knowledge of the ideas and concepts covered in Lesson 3 of the Data Literacy Level 2 course. You will need a score of 80% or better on each of the quizzes in order to earn your Data Explorer badge. At the end of the quiz you will be given a chance to
- [Level 2 Lesson 2 Quiz](https://dataliteracy.com/courses/data-literacy-level-2-v2/lessons/level-2-lesson-2/quizzes/level-2-lesson-2-quiz/) - Let's test your knowledge of the ideas and concepts covered in Lesson 2 of the Data Literacy Level 2 course. You will need a score of 80% or better on each of the quizzes in order to earn your Data Explorer badge. At the end of the quiz you will be given a chance to
- [Level 2 Lesson 1 Quiz](https://dataliteracy.com/courses/data-literacy-level-2-v2/lessons/level-2-lesson-1/quizzes/level-2-lesson-1-quiz/) - Let’s test your knowledge of the ideas and concepts covered in Lesson 1 of the Data Literacy Level 2 course. You will need a score of 80% or better on each of the quizzes in order to earn your Data Explorer badge. At the end of the quiz you will be given a chance to
- [Data & AI Literacy Pre-Test for 3Degrees](https://dataliteracy.com/courses/data-ai-literacy-pre-test-for-3degrees/quizzes/data-ai-literacy-pre-test-for-3degrees/)
- [Data Literacy Post-Test for 3Degrees](https://dataliteracy.com/quizzes/data-literacy-post-test-for-3degrees/)
- [Lesson 2 Quiz](https://dataliteracy.com/courses/advancing-responsible-ai/lessons/lesson-2-transparent-and-explainable-ai/quizzes/advancing-responsible-ai-lesson-2-quiz/) - You need a score of 80% or higher to pass the quiz. You can retake the quiz as many times as needed to pass. Click the "Start Quiz" button below when you're ready to begin.
- [Data Literacy Individual Objective Assessment](https://dataliteracy.com/courses/data-literacy-individual-objective-assessment/quizzes/data-literacy-individual-objective-assessment/) - Click the Start Quiz button to officially begin the assessment. After clicking Start Quiz, the 20 minute timer will begin.
- [Data Citizen Placement Assessment](https://dataliteracy.com/courses/data-citizen-placement-assessment/quizzes/data-citizen-placement-assessment/) - Once you're ready, click the "Start Quiz" button below and a timer will begin counting down. Best of luck!
- [Advancing Responsible AI Pre-Course Knowledge Check](https://dataliteracy.com/courses/advancing-responsible-ai/lessons/introduction-to-advancing-responsible-ai/quizzes/advancing-responsible-ai-pre-course-knowledge-check/) - Welcome to the Advancing Responsible AI pre-course knowledge check. This brief 10-question assessment is designed to gauge your initial understanding of the core concepts we'll cover in the course. Don't worry if you find some questions challenging, as this check is meant to highlight your learning journey, from start to finish. Whether you're new to AI
- [Lesson 6 Quiz](https://dataliteracy.com/courses/advancing-responsible-ai/lessons/lesson-6-ais-impact-on-society-and-the-environment/quizzes/advancing-responsible-ai-lesson-6-quiz/) - You need a score of 80% or higher to pass the quiz. You can retake the quiz as many times as needed to pass. Click the "Start Quiz" button below when you're ready to begin.
- [Lesson 5 Quiz](https://dataliteracy.com/courses/advancing-responsible-ai/lessons/lesson-5-accountability-and-ai-governance/quizzes/advancing-responsible-ai-lesson-5-quiz/) - You need a score of 80% or higher to pass the quiz. You can retake the quiz as many times as needed to pass. Click the "Start Quiz" button below when you're ready to begin.
- [Lesson 4 Quiz](https://dataliteracy.com/courses/advancing-responsible-ai/lessons/lesson-4-ai-safety-and-security/quizzes/advancing-responsible-ai-lesson-4-quiz/) - You need a score of 80% or higher to pass the quiz. You can retake the quiz as many times as needed to pass. Click the "Start Quiz" button below when you're ready to begin.
- [Lesson 3 Quiz](https://dataliteracy.com/courses/advancing-responsible-ai/lessons/lesson-3-privacy-and-data-protection-in-ai/quizzes/advancing-responsible-ai-lesson-3-quiz/) - You need a score of 80% or higher to pass the quiz. You can retake the quiz as many times as needed to pass. Click the "Start Quiz" button below when you're ready to begin.
- [Lesson 1 Quiz](https://dataliteracy.com/courses/advancing-responsible-ai/lessons/lesson-1-bias-and-fairness-in-ai/quizzes/advancing-responsible-ai-lesson-1-quiz/) - You need a score of 80% or higher to pass the quiz. You can retake the quiz as many times as needed to pass. Click the "Start Quiz" button below when you're ready to begin.
- [Advancing Responsible AI Post-Course Knowledge Check](https://dataliteracy.com/courses/advancing-responsible-ai/lessons/wrapping-up-advancing-responsible-ai/quizzes/advancing-responsible-ai-post-course-knowledge-check/) - Congratulations on reaching the end of the Advancing Responsible AI course! It's time for a 10-question knowledge check to assess your grasp of the key concepts we've explored. This post-course assessment aims to showcase your progress from beginning to end. Remember, it's a one-time opportunity, so give your best guess if you're uncertain about any question.
- [Fundamentals Lesson 4 Quiz](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-four-types-of-data-scales/quizzes/fundamentals-lesson-4-quiz/) - Let's test your knowledge about ideas and concepts covered in Lesson 4 of the Data Literacy Fundamentals course. You will need a score of 80% or better on each of the quizzes in order to earn your Data Citizen badge. At the end of the quiz you will be given a chance to review your
- [Lesson 8 Quiz](https://dataliteracy.com/courses/data-literacy-fundamentals-v2/lessons/eight-questions-to-ask-upfront/quizzes/lesson-eight-quiz/) - You will need a score of 80% or better on each of the quizzes in order to earn your certificate of completion. You can retake the quiz if necessary, and there is no limit on the number of retries.
- [Level 1 Post-Course Knowledge Check](https://dataliteracy.com/courses/data-literacy-level-1-v2/lessons/wrapping-up-data-literacy-level-1-v2/quizzes/level-1-post-course-knowledge-check-2/) - Congratulations on reaching the end of the Data Literacy Level 1 course! It's time for a 10-question knowledge check to assess your grasp of the key concepts we've explored. This post-course assessment aims to showcase your progress from beginning to end. Remember, it's a one-time opportunity, so give your best guess if you're uncertain about any
- [Level 1 Lesson 8 Quiz](https://dataliteracy.com/courses/data-literacy-level-1-v2/lessons/level-1-lesson-8/quizzes/level-1-lesson-8-quiz-v2/) - You need a score of 80% or higher to pass the quiz. You can retake the quiz as many times as needed to pass. Click the "Start Quiz" button below when you're ready to begin.
- [Level 1 Lesson 7 Quiz](https://dataliteracy.com/courses/data-literacy-level-1-v2/lessons/level-1-lesson-7/quizzes/level-1-lesson-7-quiz-v2/) - You need a score of 80% or higher to pass the quiz. You can retake the quiz as many times as needed to pass. Click the "Start Quiz" button below when you're ready to begin.
- [Level 1 Lesson 6 Quiz](https://dataliteracy.com/courses/data-literacy-level-1-v2/lessons/level-1-lesson-6/quizzes/level-1-lesson-6-quiz-v2/) - You need a score of 80% or higher to pass the quiz. You can retake the quiz as many times as needed to pass. Click the "Start Quiz" button below when you're ready to begin.
- [Level 1 Lesson 5 Quiz](https://dataliteracy.com/courses/data-literacy-level-1-v2/lessons/level-1-lesson-5/quizzes/level-1-lesson-5-quiz-v2/) - You need a score of 80% or higher to pass the quiz. You can retake the quiz as many times as needed to pass. Click the "Start Quiz" button below when you're ready to begin.
- [Level 1 Lesson 3 Quiz](https://dataliteracy.com/courses/data-literacy-level-1-v2/lessons/level-1-lesson-3/quizzes/level-1-lesson-3-quiz-v2/) - You need a score of 80% or higher to pass the quiz. You can retake the quiz as many times as needed to pass. Click the "Start Quiz" button below when you're ready to begin.
- [Level 1 Lesson 4 Quiz](https://dataliteracy.com/courses/data-literacy-level-1-v2/lessons/level-1-lesson-4/quizzes/level-1-lesson-4-quiz-v2/) - You need a score of 80% or higher to pass the quiz. You can retake the quiz as many times as needed to pass. Click the "Start Quiz" button below when you're ready to begin.
- [Level 1 Lesson 2 Quiz](https://dataliteracy.com/courses/data-literacy-level-1-v2/lessons/level-1-lesson-2/quizzes/level-1-lesson-2-quiz-v2/) - You need a score of 80% or higher to pass the quiz. You can retake the quiz as many times as needed to pass. Click the "Start Quiz" button below when you're ready to begin.
- [Level 1 Lesson 1 Quiz](https://dataliteracy.com/courses/data-literacy-level-1-v2/lessons/level-1-lesson-1/quizzes/level-1-lesson-1-quiz-v2/) - You need a score of 80% or higher to pass the quiz. You can retake the quiz as many times as needed to pass. Click the "Start Quiz" button below when you're ready to begin.
- [Level 1 Pre-Course Knowledge Check](https://dataliteracy.com/courses/data-literacy-level-1-v2/lessons/introduction-to-data-literacy-level-1-v2/quizzes/level-1-pre-course-knowledge-check/) - Welcome to the Data Literacy Level 1 pre-course knowledge check. This brief 10-question assessment is designed to gauge your initial understanding of the core concepts we'll cover in the course. Don't worry if you find some questions challenging, as this check is meant to highlight your learning journey, from start to finish. Whether you're new
- [Harnessing Generative AI Lesson 1 Quiz](https://dataliteracy.com/courses/harnessing-generative-ai/lessons/lesson-1-what-is-generative-ai/quizzes/level-1-lesson-1-quiz-5/) - You need a score of 80% or higher to pass the quiz. You can retake the quiz as many times as needed to pass. Click the "Start Quiz" button below when you're ready to begin.
- [Harnessing Generative AI Post-Course Knowledge Check](https://dataliteracy.com/courses/harnessing-generative-ai/lessons/wrapping-up-harnessing-generative-ai/quizzes/level-1-post-course-knowledge-check/) - Congratulations on reaching the end of the Harnessing Generative AI course! It's time for a 10-question knowledge check to assess your grasp of the key concepts we've explored. This post-course assessment aims to showcase your progress from beginning to end. Remember, it's a one-time opportunity, so give your best guess if you're uncertain about any question.
- [Harnessing Generative AI Pre-Course Knowledge Check](https://dataliteracy.com/courses/harnessing-generative-ai/lessons/introduction-to-harnessing-generative-ai/quizzes/level-1-pre-course-knowledge-check-2/) - Welcome to the Harnessing Generative AI pre-course knowledge check. This brief 10-question assessment is designed to gauge your initial understanding of the core concepts we'll cover in the course. Don't worry if you find some questions challenging, as this check is meant to highlight your learning journey, from start to finish. Whether you're new to
- [Harnessing Generative AI Lesson 6 Quiz](https://dataliteracy.com/courses/harnessing-generative-ai/lessons/lesson-6-ethical-considerations-of-generative-ai/quizzes/harnessing-generative-ai-lesson-1-quiz-5/) - You need a score of 80% or higher to pass the quiz. You can retake the quiz as many times as needed to pass. Click the "Start Quiz" button below when you're ready to begin.
- [Harnessing Generative AI Lesson 5 Quiz](https://dataliteracy.com/courses/harnessing-generative-ai/lessons/lesson-5-avoiding-generative-ai-pitfalls/quizzes/harnessing-generative-ai-lesson-1-quiz-4/) - You need a score of 80% or higher to pass the quiz. You can retake the quiz as many times as needed to pass. Click the "Start Quiz" button below when you're ready to begin.
- [Harnessing Generative AI Lesson 4 Quiz](https://dataliteracy.com/courses/harnessing-generative-ai/lessons/lesson-4-strategic-applications-of-generative-ai/quizzes/harnessing-generative-ai-lesson-1-quiz-3/) - You need a score of 80% or higher to pass the quiz. You can retake the quiz as many times as needed to pass. Click the "Start Quiz" button below when you're ready to begin.
- [Harnessing Generative AI Lesson 3 Quiz](https://dataliteracy.com/courses/harnessing-generative-ai/lessons/lesson-3-effective-prompting-of-generative-ai/quizzes/harnessing-generative-ai-lesson-1-quiz-2/) - You need a score of 80% or higher to pass the quiz. You can retake the quiz as many times as needed to pass. Click the "Start Quiz" button below when you're ready to begin.
- [Harnessing Generative AI Lesson 2 Quiz](https://dataliteracy.com/courses/harnessing-generative-ai/lessons/lesson-2-the-technology-behind-generative-ai/quizzes/harnessing-generative-ai-lesson-1-quiz/) - You need a score of 80% or higher to pass the quiz. You can retake the quiz as many times as needed to pass. Click the "Start Quiz" button below when you're ready to begin.
- [Lesson 6 Quiz](https://dataliteracy.com/courses/ai-literacy-fundamentals/lessons/ai-benefits-and-concerns-2/quizzes/lesson-6-quiz-2/) - You need a score of 80% or higher to pass the quiz. You can retake the quiz as many times as needed to pass. Click the "Start Quiz" button below when you're ready to begin.
- [Lesson 4 Quiz](https://dataliteracy.com/courses/ai-literacy-fundamentals/lessons/a-primer-on-deep-learning/quizzes/lesson-4-quiz-2/) - You need a score of 80% or higher to pass the quiz. You can retake the quiz as many times as needed to pass. Click the "Start Quiz" button below when you're ready to begin.
- [Lesson 5 Quiz](https://dataliteracy.com/courses/ai-literacy-fundamentals/lessons/ai-benefits-and-concerns/quizzes/lesson-5-quiz-2/) - You need a score of 80% or higher to pass the quiz. You can retake the quiz as many times as needed to pass. Click the "Start Quiz" button below when you're ready to begin.
- [Lesson 3 Quiz](https://dataliteracy.com/courses/ai-literacy-fundamentals/lessons/machine-learning-basics/quizzes/lesson-3-quiz-2/) - You need a score of 80% or higher to pass the quiz. You can retake the quiz as many times as needed to pass. Click the "Start Quiz" button below when you're ready to begin.
- [Lesson 2 Quiz](https://dataliteracy.com/courses/ai-literacy-fundamentals/lessons/a-brief-history-of-ai/quizzes/lesson-2-quiz-2/) - You need a score of 80% or higher to pass the quiz. You can retake the quiz as many times as needed to pass. Click the "Start Quiz" button below when you're ready to begin.
- [Lesson 1 Quiz](https://dataliteracy.com/courses/ai-literacy-fundamentals/lessons/what-is-ai/quizzes/lesson-1-quiz-2/) - You need a score of 80% or higher to pass the quiz. You can retake the quiz as many times as needed to pass. Click the "Start Quiz" button below when you're ready to begin.
- [Pre-Course Knowledge Check](https://dataliteracy.com/courses/ai-literacy-fundamentals/lessons/introduction-to-ai-literacy-fundamentals/quizzes/ai-literacy-fundamentals-pre-course-knowledge-check/) - Welcome to the AI Literacy Fundamentals pre-course knowledge check. This brief 10-question assessment is designed to gauge your initial understanding of the core concepts we'll cover in the course. Don't worry if you find some questions challenging, as this check is meant to highlight your learning journey, from start to finish. Whether you're new to
- [Post-Course Knowledge Check](https://dataliteracy.com/courses/ai-literacy-fundamentals/lessons/wrapping-up-ai-literacy-fundamentals/quizzes/ai-literacy-fundamentals-post-course-knowledge-check/) - Congratulations on reaching the end of the AI Literacy Fundamentals course! It's time for a 10-question knowledge check to assess your grasp of the key concepts we've explored. This post-course assessment aims to showcase your progress from beginning to end. Remember, it's a one-time opportunity, so give your best guess if you're uncertain about any question.
- [Data Literacy Fundamentals Pre-Course Knowledge Check](https://dataliteracy.com/courses/data-literacy-fundamentals-v2/lessons/introduction-to-fundamentals/quizzes/pre-course-knowledge-check/) - Welcome to the Data Literacy Fundamentals pre-course knowledge check. This brief 10-question assessment is designed to gauge your initial understanding of the core concepts we'll cover in the course. Don't worry if you find some questions challenging, as this check is meant to highlight your learning journey, from start to finish. Whether you're new to
- [Lesson 7 Quiz](https://dataliteracy.com/courses/data-literacy-fundamentals-v2/lessons/seven-groups-of-data-activities/quizzes/lesson-seven-quiz/) - You will need a score of 80% or better on each of the quizzes in order to earn your certificate of completion. You can retake the quiz if necessary, and there is no limit on the number of retries.
- [Lesson 6 Quiz](https://dataliteracy.com/courses/data-literacy-fundamentals-v2/lessons/six-ways-of-displaying-data/quizzes/lesson-six-quiz/) - You will need a score of 80% or better on each of the quizzes in order to earn your certificate of completion. You can retake the quiz if necessary, and there is no limit on the number of retries.
- [Lesson 5 Quiz](https://dataliteracy.com/courses/data-literacy-fundamentals-v2/lessons/five-forms-of-data-analysis/quizzes/lesson-five-quiz/) - You will need a score of 80% or better on each of the quizzes in order to earn your certificate of completion. You can retake the quiz if necessary, and there is no limit on the number of retries.
- [Lesson 4 Quiz](https://dataliteracy.com/courses/data-literacy-fundamentals-v2/lessons/four-types-of-data-scales/quizzes/lesson-four-quiz/) - You will need a score of 80% or better on each of the quizzes in order to earn your certificate of completion. You can retake the quiz if necessary, and there is no limit on the number of retries.
- [Lesson 1 Quiz](https://dataliteracy.com/courses/data-literacy-fundamentals-v2/lessons/one-overall-goal-of-data/quizzes/lesson-one-quiz/) - You will need a score of 80% or better on each of the quizzes in order to earn your certificate of completion. You can retake the quiz if necessary, and there is no limit on the number of retries.
- [Lesson 2 Quiz](https://dataliteracy.com/courses/data-literacy-fundamentals-v2/lessons/two-systems-of-thinking/quizzes/lesson-two-quiz/) - You will need a score of 80% or better on each of the quizzes in order to earn your certificate of completion. You can retake the quiz if necessary, and there is no limit on the number of retries.
- [Lesson 3 Quiz](https://dataliteracy.com/courses/data-literacy-fundamentals-v2/lessons/three-domains-of-application/quizzes/lesson-three-quiz/) - You will need a score of 80% or better on each of the quizzes in order to earn your certificate of completion. You can retake the quiz if necessary, and there is no limit on the number of retries.
- [Data Literacy Fundamentals Post-Course Knowledge Check](https://dataliteracy.com/courses/data-literacy-fundamentals-v2/lessons/wrapping-up/quizzes/post-course-knowledge-check/) - Congratulations on reaching the end of the Data Literacy Fundamentals course! It's time for a 10-question knowledge check to assess your grasp of the key concepts we've explored. This post-course assessment aims to showcase your progress from beginning to end. Remember, it's a one-time opportunity, so give your best guess if you're uncertain about any
- [Data Explorer Assessment](https://dataliteracy.com/courses/data-explorer-assessment/quizzes/data-explorer-assessment/) - Once you're ready, click the "Start Quiz" button below and a timer will begin counting down. Best of luck!
- [Data Explorer Placement Assessment](https://dataliteracy.com/courses/data-explorer-placement-assessment/quizzes/data-explorer-placement-assessment/) - Once you're ready, click the "Start Quiz" button below and a timer will begin counting down. Best of luck!
- [Visual Interpreter Assessment](https://dataliteracy.com/courses/visual-interpreter-assessment/quizzes/visual-interpreter-assessment/) - Once you're ready, click the "Start Quiz" button below and a timer will begin counting down. Best of luck!
- [Visual Interpreter Placement Assessment](https://dataliteracy.com/courses/visual-interpreter-placement-assessment/quizzes/visual-interpreter-placement-assessment/) - Once you're ready, click the "Start Quiz" button below and a timer will begin counting down. Best of luck!
- [Data Citizen Assessment](https://dataliteracy.com/courses/data-citizen-assessment/quizzes/data-citizen-assessment/) - Once you're ready, click the "Start Quiz" button below and a timer will begin counting down. Best of luck!
- [Data Literacy Survey for Intuit](https://dataliteracy.com/courses/data-literacy-assessment-for-intuit/quizzes/data-literacy-survey-for-intuit/) - Welcome to this short survey (4 questions) that will ask you to rate your level of confidence in four different types of data activities. This portion of the quiz is entirely subjective, and there are no right or wrong answers to any of the questions.
- [Data Literacy Quiz for Intuit](https://dataliteracy.com/courses/data-literacy-assessment-for-intuit/quizzes/data-literacy-training-quiz-for-intuit/) - Welcome to this short assessment that will test your data literacy competencies (20 quiz questions). These 20 questions have right and wrong answers, including single choice, multiple choice, fill in the blank, and sorting style questions. Feel free to provide the best answers you can to each of these questions, and good luck!
- [Data Literacy Fundamentals Pre-Course Knowledge Check](https://dataliteracy.com/courses/data-literacy-fundamentals-pretest/lessons/data-literacy-fundamentals-pre-course-knowledge-check/quizzes/data-literacy-fundamentals-pre-course-knowledge-check/) - The knowledge check consists of 10 questions that will assess your understanding of key concepts covered in Data Literacy Fundamentals: Understanding the Power & Value of Data. Our introductory course helps you become more familiar with the power and value of data, and how teams work together to put it to use. The quiz can
- [Data Literacy for Leaders: Lesson 3 Quiz](https://dataliteracy.com/courses/data-literacy-for-leaders/lessons/the-data-factor/quizzes/lesson-3-quiz/) - Let’s test your understanding of the guiding principles of data. There are seven questions and you'll need a score of 70% or better in order to pass the quiz so that you can earn your course completion certificate at the end. After you’ve finished the quiz, you’ll be given a chance to review your answers
- [Data Literacy for Leaders: Lesson 5 Quiz](https://dataliteracy.com/courses/data-literacy-for-leaders/lessons/the-people-factor/quizzes/lesson-5-quiz/) - Let’s test your understanding of the guiding principles of people. There are seven questions and you'll need a score of 70% or better in order to pass the quiz so that you can earn your course completion certificate at the end. After you’ve finished the quiz, you’ll be given a chance to review your answers
- [Data Literacy for Leaders: Lesson 7 Quiz](https://dataliteracy.com/courses/data-literacy-for-leaders/lessons/the-culture-factor/quizzes/lesson-7-quiz/) - Let’s test your understanding of the guiding principles of culture. There are seven questions and you'll need a score of 70% or better in order to pass the quiz so that you can earn your course completion certificate at the end. After you’ve finished the quiz, you’ll be given a chance to review your answers
- [Data Literacy for Leaders: Lesson 6 Quiz](https://dataliteracy.com/courses/data-literacy-for-leaders/lessons/the-process-factor/quizzes/lesson-6-quiz/) - Let’s test your understanding of the guiding principles of process. There are seven questions and you'll need a score of 70% or better in order to pass the quiz so that you can earn your course completion certificate at the end. After you’ve finished the quiz, you’ll be given a chance to review your answers
- [Data Literacy for Leaders: Lesson 4 Quiz](https://dataliteracy.com/courses/data-literacy-for-leaders/lessons/the-technology-factor/quizzes/lesson-4-quiz/) - Let’s test your understanding of the guiding principles of data technology. There are seven questions and you'll need a score of 70% or better in order to pass the quiz so that you can earn your course completion certificate at the end. After you’ve finished the quiz, you’ll be given a chance to review your
- [Data Literacy for Leaders: Lesson 2 Quiz](https://dataliteracy.com/courses/data-literacy-for-leaders/lessons/the-purpose-factor/quizzes/lesson-2-quiz/) - Let’s test your understanding of the guiding principles to aligning data with purpose. There are seven questions and you'll need a score of 70% or better in order to pass the quiz so that you can earn your course completion certificate at the end. After you’ve finished the quiz, you’ll be given a chance to
- [Data Literacy for Leaders: Lesson 1 Quiz](https://dataliteracy.com/courses/data-literacy-for-leaders/lessons/the-ethics-factor/quizzes/lesson-1-quiz/) - Let’s test your understanding of the guiding principles of data ethics. There are seven questions and you'll need a score of 70% or better in order to pass the quiz so that you can earn your course completion certificate at the end. After you’ve finished the quiz, you’ll be given a chance to review your
- [Working with Data Professionals: Lesson 5 Quiz](https://dataliteracy.com/courses/working-with-data-professionals/lessons/5-building-a-partnership-with-data-professionals/quizzes/working-with-data-professionals-lesson-5-quiz/) - Let’s test your knowledge about ideas and concepts covered in Lesson 5. You'll need a score of 80% or better in order to pass the quiz so that you can earn your course completion certificate at the end. After you've finished the quiz, you'll be given a chance to review your answers and retake it
- [Working with Data Professionals: Lesson 4 Quiz](https://dataliteracy.com/courses/working-with-data-professionals/lessons/4-measuring-the-performance-of-data-professionals-parts-1-2/quizzes/working-with-data-professionals-lesson-4-quiz/) - Let’s test your knowledge about ideas and concepts covered in Lesson 4. You'll need a score of 80% or better in order to pass the quiz so that you can earn your course completion certificate at the end. After you've finished the quiz, you'll be given a chance to review your answers and retake it
- [Working with Data Professionals: Lesson 3 Quiz](https://dataliteracy.com/courses/working-with-data-professionals/lessons/3-managing-a-team-of-data-professionals/quizzes/working-with-data-professionals-lesson-3-quiz/) - Let’s test your knowledge about ideas and concepts covered in Lesson 3. You'll need a score of 80% or better in order to pass the quiz so that you can earn your course completion certificate at the end. After you've finished the quiz, you'll be given a chance to review your answers and retake it
- [Working with Data Professionals: Lesson 2 Quiz](https://dataliteracy.com/courses/working-with-data-professionals/lessons/2-how-to-hire-a-data-professional-v-2-0/quizzes/working-with-data-professionals-lesson-2-quiz/) - Let’s test your knowledge about ideas and concepts covered in Lesson 2. You'll need a score of 80% or better in order to pass the quiz so that you can earn your course completion certificate at the end. After you've finished the quiz, you'll be given a chance to review your answers and retake it
- [Working with Data Professionals: Lesson 1 Quiz](https://dataliteracy.com/courses/working-with-data-professionals/lessons/1-what-do-data-professionals-do/quizzes/working-with-data-professionals-lesson-1-quiz/) - Let’s test your knowledge about ideas and concepts covered in Lesson 1. You'll need a score of 80% or better in order to pass the quiz so that you can earn your course completion certificate at the end. After you've finished the quiz, you'll be given a chance to review your answers and retake it
- [ChatGPT Basics: Lesson 1 Quiz](https://dataliteracy.com/courses/chatgpt-basics/lessons/1-the-rise-of-chatgpt/quizzes/chatgpt-basics-lesson-1-quiz/) - Let’s test your knowledge about ideas and concepts covered in Lesson 1. You'll need a score of 80% or better in order to pass the quiz so that you can earn your course completion certificate at the end. After you've finished the quiz, you'll be given a chance to review your answers and retake it
- [ChatGPT Basics: Lesson 2 Quiz](https://dataliteracy.com/courses/chatgpt-basics/lessons/2-what-is-chatgpt/quizzes/chatgpt-basics-lesson-2-quiz/) - Let’s test your knowledge about ideas and concepts covered in Lesson 2. You'll need a score of 80% or better in order to pass the quiz so that you can earn your course completion certificate at the end. After you've finished the quiz, you'll be given a chance to review your answers and retake it
- [ChatGPT Basics: Lesson 3 Quiz](https://dataliteracy.com/courses/chatgpt-basics/lessons/3-chatgpt-warnings-and-caveats/quizzes/chatgpt-basics-lesson-3-quiz/) - Let’s test your knowledge about ideas and concepts covered in Lesson 3. You'll need a score of 80% or better in order to pass the quiz so that you can earn your course completion certificate at the end. After you've finished the quiz, you'll be given a chance to review your answers and retake it
- [ChatGPT Basics: Lesson 4 Quiz](https://dataliteracy.com/courses/chatgpt-basics/lessons/3-getting-started-with-chatgpt/quizzes/chatgpt-basics-lesson-4-quiz/) - Let’s test your knowledge about ideas and concepts covered in Lesson 4. You'll need a score of 80% or better in order to pass the quiz so that you can earn your course completion certificate at the end. After you've finished the quiz, you'll be given a chance to review your answers and retake it
- [Chat GPT Basics: Lesson 5 Quiz](https://dataliteracy.com/courses/chatgpt-basics/lessons/4-creating-prompts-for-chatgpt/quizzes/chat-gpt-basics-lesson-5-quiz/) - Let’s test your knowledge about ideas and concepts covered in Lesson 5. You'll need a score of 80% or better in order to pass the quiz so that you can earn your course completion certificate at the end. After you've finished the quiz, you'll be given a chance to review your answers and retake it
- [MATURE Phase Quiz](https://dataliteracy.com/courses/data-literacy-level-2/lessons/4-m-the-mature-phase/quizzes/mature-phase-quiz/) - Let's test your knowledge of the ideas and concepts covered in Lesson 4 of the Data Literacy Level 2 course. You will need a score of 80% or better on each of the quizzes in order to earn your Data Explorer badge. At the end of the quiz you will be given a chance to
- [DISCOVER Phase Quiz](https://dataliteracy.com/courses/data-literacy-level-2/lessons/3-d-the-discover-phase/quizzes/discover-phase-quiz/) - Let's test your knowledge of the ideas and concepts covered in Lesson 3 of the Data Literacy Level 2 course. You will need a score of 80% or better on each of the quizzes in order to earn your Data Explorer badge. At the end of the quiz you will be given a chance to
- [SHAPE Phase Quiz](https://dataliteracy.com/courses/data-literacy-level-2/lessons/2-s-the-shape-phase/quizzes/level-2-phase-2-quiz/) - Let's test your knowledge of the ideas and concepts covered in Lesson 2 of the Data Literacy Level 2 course. You will need a score of 80% or better on each of the quizzes in order to earn your Data Explorer badge. At the end of the quiz you will be given a chance to
- [WONDER Phase Quiz](https://dataliteracy.com/courses/data-literacy-level-2/lessons/1-w-the-wonder-phase/quizzes/level-2-phase-1-quiz/) - Let's test your knowledge of the ideas and concepts covered in Lesson 1 of the Data Literacy Level 2 course. You will need a score of 80% or better on each of the quizzes in order to earn your Data Explorer badge. At the end of the quiz you will be given a chance to
- [Fundamentals Lesson 3 Quiz](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-three-domains-of-application/quizzes/fundamentals-lesson-3-quiz/) - Let's test your knowledge about ideas and concepts covered in Lesson 3 of the Data Literacy Fundamentals course. You will need a score of 80% or better on each of the quizzes in order to earn your Data Citizen badge. At the end of the quiz you will be given a chance to review your
- [Fundamentals Lesson 5 Quiz](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-five-forms-of-data-analysis/quizzes/fundamentals-lesson-5-quiz/) - Let's test your knowledge about ideas and concepts covered in Lesson 5 of the Data Literacy Fundamentals course. You will need a score of 80% or better on each of the quizzes in order to earn your Data Citizen badge. At the end of the quiz you will be given a chance to review your
- [Fundamentals Lesson 6 Quiz](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-six-ways-of-displaying-data/quizzes/fundamentals-lesson-6-quiz/) - Let's test your knowledge about ideas and concepts covered in Lesson 6 of the Data Literacy Fundamentals course. You will need a score of 80% or better on each of the quizzes in order to earn your Data Citizen badge. At the end of the quiz you will be given a chance to review your
- [Fundamentals Lesson 7 Quiz](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-seven-groups-of-data-activities/quizzes/fundamentals-lesson-7-quiz/) - Let's test your knowledge about ideas and concepts covered in Lesson 7 of the Data Literacy Fundamentals course. You will need a score of 80% or better on each of the quizzes in order to earn your Data Citizen badge. At the end of the quiz you will be given a chance to review your
- [Fundamentals Lesson 8 Quiz](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-eight-questions-to-ask-upfront/quizzes/fundamentals-lesson-8-quiz/) - Let's test your knowledge about ideas and concepts covered in Lesson 8 of the Data Literacy Fundamentals course. You will need a score of 80% or better on each of the quizzes in order to earn your Data Citizen badge. At the end of the quiz you will be given a chance to review your
- [Fundamentals Lesson 1 Quiz](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-one-overall-goal-of-data/quizzes/fundamentals-lesson-1-quiz/) - Let's test your knowledge about ideas and concepts covered in Lesson 1 of the Data Literacy Fundamentals course. You will need a score of 80% or better on each of the quizzes in order to earn your Data Citizen badge. At the end of the quiz you will be given a chance to review your
- [Fundamentals Lesson 2 Quiz](https://dataliteracy.com/courses/data-literacy-fundamentals/lessons/the-two-systems-of-thinking/quizzes/fundamentals-lesson-2-quiz/) - Let's test your knowledge about ideas and concepts covered in Lesson 2 of the Data Literacy Fundamentals course. You will need a score of 80% or better on each of the quizzes in order to earn your Data Citizen badge. At the end of the quiz you will be given a chance to review your
- [Data Literacy Level 2 Post-Course Knowledge Check](https://dataliteracy.com/courses/data-literacy-level-2-post-course-knowledge-check/lessons/data-literacy-level-2-post-course-knowledge-check/quizzes/data-literacy-level-2-post-course-knowledge-check/)
- [Data Literacy Level 2 Pre-Course Knowledge Check](https://dataliteracy.com/courses/data-literacy-level-2-pre-course-knowledge-check/lessons/data-literacy-level-2-pre-course-knowledge-check/quizzes/data-literacy-level-2-pre-course-knowledge-check/)
- [Data Literacy Level 1 Pre-Course Knowledge Check](https://dataliteracy.com/courses/data-literacy-level-1-pre-course-knowledge-check/lessons/data-literacy-level-1-pre-course-knowledge-check/quizzes/data-literacy-level-1-pre-course-knowledge-check/)
- [Quiz](https://dataliteracy.com/quizzes/quiz-4/)
- [Data Literacy Level 1 Post-Course Knowledge Check](https://dataliteracy.com/courses/data-literacy-level-1-final-quiz/quizzes/data-literacy-level-1-post-course-knowledge-check/)
- [Data Literacy Fundamentals Post-Course Knowledge Check](https://dataliteracy.com/courses/data-literacy-fundamentals-final-quiz/quizzes/data-literacy-fundamentals-post-course-knowledge-check/)
- [Quiz](https://dataliteracy.com/quizzes/quiz-2/)
- [Quiz](https://dataliteracy.com/quizzes/quiz/)
- [Data Literacy Fundamentals Placement Quiz](https://dataliteracy.com/courses/data-literacy-placement-assessment/lessons/fundamentals-placement-quiz/quizzes/data-literacy-fundamentals-placement-quiz/) - Test your knowledge to see if the Data Literacy Fundamentals course is suitable for you!
- [Data Literacy Level 1 Placement Quiz](https://dataliteracy.com/courses/data-literacy-placement-assessment/lessons/level-1-placement-quiz/quizzes/data-literacy-level-1-placement-quiz/) - Test your knowledge with these 10 quiz questions to see whether the Data Literacy Level 1 course is right for you. Read each question carefully and choose one or more of the corresponding answer choices. You have 15 minutes to complete this portion of the placement assessment. Good luck on this second of two parts
- [17 Key Traits Practice](https://dataliteracy.com/quizzes/17-key-traits-practice/)
- [Level 1 Lesson 4 Quiz](https://dataliteracy.com/courses/data-literacy-level-1/lessons/part-to-whole/quizzes/level-1-lesson-4-quiz/)
- [Data Literacy Fundamentals Placement Assessment - NBB](https://dataliteracy.com/courses/placement-assessment-nbb/lessons/fundamentals-placement-assessment-nbb/quizzes/data-literacy-fundamentals-placement-assessment-nbb/) - Test your knowledge to see if the Data Literacy Fundamentals course is suitable for you!
- [Data Literacy Level 1 Placement Assessment - NBB](https://dataliteracy.com/courses/placement-assessment-nbb/lessons/level-1-placement-assessment-nbb/quizzes/data-literacy-level-1-placement-assessment-nbb/) - Test your knowledge with these 10 quiz questions to see whether the Data Literacy Level 1 course is right for you. Read each question carefully and choose one or more of the corresponding answer choices. You have 15 minutes to complete this portion of the placement assessment. Good luck on this second of two parts
- [Level 1 Lesson 8 Quiz](https://dataliteracy.com/courses/data-literacy-level-1/lessons/spacial-and-geospacial/quizzes/level-1-lesson-8-quiz/)
- [Level 1 Lesson 7 Quiz](https://dataliteracy.com/courses/data-literacy-level-1/lessons/correlation/quizzes/level-1-lesson-7-quiz/)
- [Level 1 Lesson 6 Quiz](https://dataliteracy.com/courses/data-literacy-level-1/lessons/distribution-and-variation/quizzes/level-1-lesson-6-quiz/)
- [Level 1 Lesson 5 Quiz](https://dataliteracy.com/courses/data-literacy-level-1/lessons/change-over-time/quizzes/level-1-lesson-5-quiz/)
- [Level 1 Lesson 3 Quiz](https://dataliteracy.com/courses/data-literacy-level-1/lessons/comparing-quantities/quizzes/level-1-lesson-3-quiz/)
- [Level 1 Lesson 2 Quiz](https://dataliteracy.com/courses/data-literacy-level-1/lessons/figures-and-tables/quizzes/level-1-lesson-2-quiz/)
- [Level 1 Lesson 1 Quiz](https://dataliteracy.com/courses/data-literacy-level-1/lessons/principles-of-visual-cognition/quizzes/level-1-lesson-1-quiz/)
- [Data Literacy Fundamentals Certification Exam](https://dataliteracy.com/quizzes/data-literacy-fundamentals-certification-exam/)
- [Topic 3.1 Exercise](https://dataliteracy.com/quizzes/topic-3-1-exercise/)

## Certificates

- [Data Storytelling for IMPACT Certificate](https://dataliteracy.com/certificates/data-storytelling-for-impact-certificate/)
- [Data Literacy Level 2 Certificate](https://dataliteracy.com/certificates/data-literacy-level-2-certificate/)
- [21 Key Traits of Data & AI Literacy Certificate](https://dataliteracy.com/certificates/21-key-traits-of-data-ai-literacy-certificate/)
- [Advancing Responsible AI Certificate](https://dataliteracy.com/certificates/advancing-responsible-ai-certificate/)
- [Harnessing Generative AI Certificate](https://dataliteracy.com/certificates/harnessing-generative-ai/)
- [Data Literacy Level 1 Certificate](https://dataliteracy.com/certificates/data-literacy-level-1-certificate/)
- [AI Literacy Fundamentals](https://dataliteracy.com/certificates/ai-literacy-fundamentals-certificate/)
- [Chart Spark Certificate](https://dataliteracy.com/certificates/chart-spark-certificate/)
- [Data Literacy Fundamentals Certificate](https://dataliteracy.com/certificates/data-literacy-fundamentals-certificate/)
- [Data Literacy for Leaders Certificate](https://dataliteracy.com/certificates/data-literacy-for-leaders-certificate/)
- [Working with Data Professionals Certificate](https://dataliteracy.com/certificates/working-with-data-professionals/)
- [ChatGPT Basics Certificate](https://dataliteracy.com/certificates/chatgpt-basics-certificate/)
- [Level 1](https://dataliteracy.com/certificates/level-1/) -  
- [Level 2 Certificate](https://dataliteracy.com/certificates/level-2-certificate/) -  
- [Level 1 Certificate](https://dataliteracy.com/certificates/level-1-certificate/)
- [17 Key Traits of Data Literacy](https://dataliteracy.com/certificates/17-key-traits-of-data-literacy/)
- [Level 1 Certificate - NBB Group Final Quiz](https://dataliteracy.com/certificates/level-1-certificate-nbb-group-final-quiz/)
- [Fundamentals Certificate - NBB Final Quiz](https://dataliteracy.com/certificates/fundamentals-certificate-nbb-final-quiz/)
- [Fundamentals Certificate](https://dataliteracy.com/certificates/fundamentals-certificate/)

## Badges

- [Data Citizen: Data Literacy Fundamentals 2.0](https://dataliteracy.com/badges/data-citizen-data-literacy-fundamentals-2-0/) - The Data Citizen Badge is awarded to those who successfully complete the Data Literacy Fundamentals 2.0 On-Demand Course: Understanding the Power & Value of Data.
- [Data Storyteller](https://dataliteracy.com/badges/data-storyteller/) - The Data Storyteller Badge is awarded to those who successfully complete the Data Storytelling for IMPACT course.
- [Data Explorer](https://dataliteracy.com/badges/data-explorer-v2/) - The Data Explorer Badge is awarded to those who successfully complete the Data Literacy Level 2 course.
- [AI Citizen](https://dataliteracy.com/badges/ai-citizen/) - The AI Citizen Badge is awarded to those who successfully complete the AI Literacy Fundamentals course.
- [Ethical AI Advocate](https://dataliteracy.com/badges/ethical-ai-advocate/) - The Ethical AI Advocate Badge is awarded to those who successfully complete the Advancing Responsible AI course.
- [Visual Interpreter: Data Literacy Level 1 v2](https://dataliteracy.com/badges/visual-interpreter-data-literacy-level-1-v2/) - The Visual Interpreter Badge is awarded to those who successfully complete the Data Literacy Level 1 on-demand course: Learning to See Data.
- [Visual Interpreter: Data Literacy Level 1](https://dataliteracy.com/badges/visual-interpreter-data-literacy-level-1/) - The Visual Interpreter Badge is awarded to those who successfully complete the Data Literacy Level 1 On-Demand Course: Learning to See Data.
- [GenAI Navigator](https://dataliteracy.com/badges/genai-navigator/) - The GenAI Navigator Badge is awarded to those who successfully complete the Harnessing Generative AI course.
- [Visual Interpreter](https://dataliteracy.com/badges/visual-interpreter/) - The Visual Interpreter badge is awarded to those who successfully pass the Visual Interpreter Assessment, a 25 question timed test that covers learnings from the Data Literacy Level 1 On-Demand Course: Learning to See Data.
- [Data Explorer](https://dataliteracy.com/badges/data-explorer/) - The Data Explorer Badge is awarded to those who successfully pass the Data Explorer Assessment, a 25 question timed test that covers learnings from the Data Literacy Level 2 On-Demand Course: Working Effectively with Data.
- [Data Citizen](https://dataliteracy.com/badges/data-citizen/) - The Data Citizen Badge is awarded to those who successfully pass the Data Citizen Assessment, a 25 question timed test that covers learnings from the Data Literacy Fundamentals On-Demand Course: Understanding the Power & Value of Data.
- [Data Explorer: Placement Assessment](https://dataliteracy.com/badges/data-explorer-placement-assessment/) - The Data Explorer Badge is awarded to those who successfully pass the Data Explorer Assessment, a 25 question timed test that covers learnings from the Data Literacy Level 2 On-Demand Course: Working Effectively with Data.
- [Visual Interpreter: Placement Assessment](https://dataliteracy.com/badges/visual-interpreter-placement-assessment/) - The Visual Interpreter badge is awarded to those who successfully pass the Visual Interpreter Assessment, a 25 question timed test that covers learnings from the Data Literacy Level 1 On-Demand Course: Learning to See Data.
- [Data Citizen: Placement Assessment](https://dataliteracy.com/badges/data-citizen-placement-assessment/) - The Data Citizen Badge is awarded to those who successfully pass the Data Citizen Assessment, a 25 question timed test that covers learnings from the Data Literacy Fundamentals On-Demand Course: Understanding the Power & Value of Data.
- [Data Explorer: Data Literacy Level 2](https://dataliteracy.com/badges/data-explorer-data-literacy-level-2/) - The Data Explorer Badge is awarded to those who successfully complete the Data Literacy Level 2 On-Demand Course: Working Effectively with Data.
- [Data Citizen: Data Literacy Fundamentals](https://dataliteracy.com/badges/data-citizen-data-literacy-fundamentals/) - The Data Citizen Badge is awarded to those who successfully complete the Data Literacy Fundamentals On-Demand Course: Understanding the Power & Value of Data.

## Group Codes

- [FSDL1](https://dataliteracy.com/ldgr_group_code/fsdl1/)
- [DL4NZDF](https://dataliteracy.com/ldgr_group_code/dl4nzdf/)
- [HZCUDATAGOV](https://dataliteracy.com/ldgr_group_code/hzcudatagov/)
- [HZCUANALYST](https://dataliteracy.com/ldgr_group_code/hzcuanalyst/)
- [INVESCOTRIAL](https://dataliteracy.com/ldgr_group_code/invescotrial/)
- [WASHSPIRITDATA](https://dataliteracy.com/ldgr_group_code/washspiritdata/)
- [PWPDL](https://dataliteracy.com/ldgr_group_code/pwpdl/)
- [CTDL4L](https://dataliteracy.com/ldgr_group_code/ctdl4l/)
- [7SET2025](https://dataliteracy.com/ldgr_group_code/7set2025/)
- [HZCULEAD](https://dataliteracy.com/ldgr_group_code/24863/)
- [DFFOUNDER](https://dataliteracy.com/ldgr_group_code/dffounder/)
- [SSCAI](https://dataliteracy.com/ldgr_group_code/sscai/)
- [IMPACTTEST](https://dataliteracy.com/ldgr_group_code/impacttest/)
- [3SETLEV3](https://dataliteracy.com/ldgr_group_code/3setlev3/)
- [YORKGROUP1](https://dataliteracy.com/ldgr_group_code/yorkgroup1/)
- [3DEGREES](https://dataliteracy.com/ldgr_group_code/3degrees/)
- [YORKAILIT](https://dataliteracy.com/ldgr_group_code/yorkailit/)
- [EBRDTRIAL](https://dataliteracy.com/ldgr_group_code/ebrdtrial/)
- [DLEVAL25](https://dataliteracy.com/ldgr_group_code/dleval25/)
- [3SET2025L2](https://dataliteracy.com/ldgr_group_code/3set2025l2/)
- [FRBOIGTEST](https://dataliteracy.com/ldgr_group_code/frboigtest/)
- [6SET2025](https://dataliteracy.com/ldgr_group_code/6set2025/)
- [MEMBERTEST](https://dataliteracy.com/ldgr_group_code/membertest/)
- [DARTMOUTHTDI](https://dataliteracy.com/ldgr_group_code/dartmouthtdi/)
- [PHOENIXCH](https://dataliteracy.com/ldgr_group_code/phoenixch/)
- [RHLEADERS](https://dataliteracy.com/ldgr_group_code/rhleaders/)
- [RHMANAGERS](https://dataliteracy.com/ldgr_group_code/rhmanagers/)
- [CTDATATEST](https://dataliteracy.com/ldgr_group_code/ctdatatest/)
- [AMGEN24DPTL1](https://dataliteracy.com/ldgr_group_code/amgen24dptl1/)
- [24ALSACFUND](https://dataliteracy.com/ldgr_group_code/24alsacfund/)
- [Q424ALSACFUND](https://dataliteracy.com/ldgr_group_code/q424alsacfund/)
- [CQI2024](https://dataliteracy.com/ldgr_group_code/cqi2024/)
- [WWDPYORK](https://dataliteracy.com/ldgr_group_code/wwdpyork/)
- [2SET2024L2](https://dataliteracy.com/ldgr_group_code/2set2024l2/)
- [GGLEVEL2](https://dataliteracy.com/ldgr_group_code/gglevel2/)
- [5SET2024](https://dataliteracy.com/ldgr_group_code/5set2024/)
- [HAWAIIDC](https://dataliteracy.com/ldgr_group_code/hawaiidc/)
- [GGPILOT](https://dataliteracy.com/ldgr_group_code/20523/)
- [RNDC](https://dataliteracy.com/ldgr_group_code/rndc/)
- [CHMCOMAHA](https://dataliteracy.com/ldgr_group_code/chmcomaha/)
- [AMGENDPT24](https://dataliteracy.com/ldgr_group_code/amgendpt24/)
- [IDBTEST](https://dataliteracy.com/ldgr_group_code/idbtest/)
- [4SET2024](https://dataliteracy.com/ldgr_group_code/4set2024/)
- [24ALSACLEVEL2](https://dataliteracy.com/ldgr_group_code/alsaclevel223q4/)
- [24ALSACLEVEL1](https://dataliteracy.com/ldgr_group_code/24alsaclevel1/)
- [SINCLAIR](https://dataliteracy.com/ldgr_group_code/sinclair/)
- [INTUIT2024](https://dataliteracy.com/ldgr_group_code/intuit2023/)
- [1SET2024L2](https://dataliteracy.com/ldgr_group_code/1set2024l2/)
- [3SET2024](https://dataliteracy.com/ldgr_group_code/3set2024/)
- [DLFSMOLLAN24](https://dataliteracy.com/ldgr_group_code/dlfsmollan24/)
- [SMOLLANDLF](https://dataliteracy.com/ldgr_group_code/smollantest/)
- [YORKLEADERS1](https://dataliteracy.com/ldgr_group_code/yorkleaders1/)
- [OTTAWAFUND](https://dataliteracy.com/ldgr_group_code/ottawafund/)
- [2SET2023](https://dataliteracy.com/ldgr_group_code/2set2023/)
- [ALSACFUND23Q3](https://dataliteracy.com/ldgr_group_code/alsacfund23q3/)
- [BFPILOT](https://dataliteracy.com/ldgr_group_code/bfpilot/)
- [HUNGERFREEPILOT](https://dataliteracy.com/ldgr_group_code/hungerfreepilot/)
- [UASUMMER23](https://dataliteracy.com/ldgr_group_code/uasummer23/)
- [MASCOPILOT](https://dataliteracy.com/ldgr_group_code/mascopilot/)
- [COCHISELEVEL2](https://dataliteracy.com/ldgr_group_code/cochiselevel2/)
- [COCHISE17KEY](https://dataliteracy.com/ldgr_group_code/cochise17key/)
- [LONDONPILOT](https://dataliteracy.com/ldgr_group_code/londonpilot/)
- [SPRINGERL2](https://dataliteracy.com/ldgr_group_code/springerl2/)
- [SPRINGER](https://dataliteracy.com/ldgr_group_code/springer/)
- [MBNCAPILOT](https://dataliteracy.com/ldgr_group_code/mbncapilot/)
- [TOGETHERCU](https://dataliteracy.com/ldgr_group_code/togethercu/)
- [CARTERCENTER3](https://dataliteracy.com/ldgr_group_code/cartercenter3/)
- [1SET2023](https://dataliteracy.com/ldgr_group_code/1set2023/)
- [DECKHANDFUND](https://dataliteracy.com/ldgr_group_code/deckhandfund/)
- [23AMGEN1](https://dataliteracy.com/ldgr_group_code/23amgen1/)
- [QUALCOMM](https://dataliteracy.com/ldgr_group_code/qualcomm/)
- [QUALCOMML2](https://dataliteracy.com/ldgr_group_code/qualcomml2/)
- [STRYKERPILOT](https://dataliteracy.com/ldgr_group_code/strykerpilot/)
- [DECKHANDPA](https://dataliteracy.com/ldgr_group_code/deckhandpa/)
- [DECKHANDL1](https://dataliteracy.com/ldgr_group_code/deckhandl1/)
- [DECKHAND6](https://dataliteracy.com/ldgr_group_code/deckhand6/)
- [BAPCO](https://dataliteracy.com/ldgr_group_code/bapco/)
- [DECKHAND4](https://dataliteracy.com/ldgr_group_code/deckhand4/)
- [DECKHAND5](https://dataliteracy.com/ldgr_group_code/deckhand5/)
- [BGC](https://dataliteracy.com/ldgr_group_code/bgc/)
- [DECKHAND3](https://dataliteracy.com/ldgr_group_code/deckhand3/)
- [Amgen6](https://dataliteracy.com/ldgr_group_code/amgen6/)
- [Amgen7](https://dataliteracy.com/ldgr_group_code/amgen7/)
- [BRIDGE](https://dataliteracy.com/ldgr_group_code/bridge/)
- [MASCO](https://dataliteracy.com/ldgr_group_code/masco/)
- [GAOGOV](https://dataliteracy.com/ldgr_group_code/gaogov/)
- [ALSAC3](https://dataliteracy.com/ldgr_group_code/alsac3/)
- [AMGEN5](https://dataliteracy.com/ldgr_group_code/amgen5/)
- [HVPILOT](https://dataliteracy.com/ldgr_group_code/hvpilot/)
- [AMGEN4](https://dataliteracy.com/ldgr_group_code/amgen4/)
- [CARTER2](https://dataliteracy.com/ldgr_group_code/carter2/)
- [ALSAC2](https://dataliteracy.com/ldgr_group_code/alsac2/)
- [DLL1Q12022](https://dataliteracy.com/ldgr_group_code/dll1q12022/)
- [ALSAC](https://dataliteracy.com/ldgr_group_code/alsac/)
- [AMGEN3DL1](https://dataliteracy.com/ldgr_group_code/amgen3dl1/)
- [DLFUNQ12022](https://dataliteracy.com/ldgr_group_code/dlfunq12022/)
- [JNJDL1](https://dataliteracy.com/ldgr_group_code/jnjdl1/)
- [AMGENDL3](https://dataliteracy.com/ldgr_group_code/amgendl3/)
- [ASUDL1](https://dataliteracy.com/ldgr_group_code/asudl1/)
- [YORKCA](https://dataliteracy.com/ldgr_group_code/yorkca/)
- [AMGENDL1](https://dataliteracy.com/ldgr_group_code/amgendl1/)
- [USPSOIG](https://dataliteracy.com/ldgr_group_code/uspsoig/)
- [TCCCohort1](https://dataliteracy.com/ldgr_group_code/tcccohort1/)
- [CARTERPILOT](https://dataliteracy.com/ldgr_group_code/carterpilot/)
- [PCU2021](https://dataliteracy.com/ldgr_group_code/pcu2021/)
- [NBBDATA1](https://dataliteracy.com/ldgr_group_code/nbbdata1/)
- [TEST](https://dataliteracy.com/ldgr_group_code/test/)
- [TESTGROUPCODE](https://dataliteracy.com/ldgr_group_code/testgroupcode/)
- [TESTCODE2](https://dataliteracy.com/ldgr_group_code/testcode2/)
- [TESTCODE6](https://dataliteracy.com/ldgr_group_code/testcode6/)
- [TESTCODE](https://dataliteracy.com/ldgr_group_code/testcode/)

## Fusion Elements

- [Review slider - 9/13/18](https://dataliteracy.com/fusion_element/review-slider-9-13-18-2/) - This course will lay a firm foundation in the core principles of AI, its history and evolution, concepts of machine learning and deep learning, everyday applications, and ethical considerations.
- [homepage alert - mobile](https://dataliteracy.com/fusion_element/homepage-alert-mobile/) - Enhance your understanding of AI’s ethical implications with this on-demand program lead by head instructor & Data Literacy CEO Ben Jones.
- [homepager alert - desktop](https://dataliteracy.com/fusion_element/homepager-alert-desktop/) - Enhance your understanding of AI’s ethical implications with this on-demand program lead by head instructor & Data Literacy CEO Ben Jones.
- [What our students are saying 2](https://dataliteracy.com/fusion_element/what-our-students-are-saying-2/) - This course will lay a firm foundation in the core principles of AI, its history and evolution, concepts of machine learning and deep learning, everyday applications, and ethical considerations.
- [CTA - Submit Your Case - 9/13/18](https://dataliteracy.com/fusion_element/cta-submit-your-case-9-13-18/) - Learn how to clean, structure and prepare dirty data for analysis, visualize and discover key insights in data, and present those insights effectively to your audience.
- [CTA - ChatGPT Basics CH3](https://dataliteracy.com/fusion_element/cta-chatgpt-basics-ch3/) - Webinar: Data Literacy — What I Wish My Leadership Had Known Earlier. Join Ben Jones, author of the new book Leading in the Age of Data, and Alli Torban as they break down the 7 factors that contribute to data-savvy leadership. We’ll share personal stories, common challenges, and offer actionable tips to help you lead with confidence and resonate positively with team members across all data literacy levels.
- [team training homepage](https://dataliteracy.com/fusion_element/team-training-homepage/) - Helping you and your team learn the language of data and AI. Training & Certification | Custom Sessions | The Data Literacy Score Assessment | Free Resources
- [preview first lesson free](https://dataliteracy.com/fusion_element/preview-first-lesson-free/) - ▶ Preview the first lesson for free
- [homepage alert - level 1 v2.0 launch](https://dataliteracy.com/fusion_element/homepage-alert-level-1-v2-0-launch/) - Launch Alert! 30% off Data Literacy Level 1 v2.0 with code LEVEL1 through Friday 9/13. Available On-Demand and Instructor-Led ×
- [harnessing generative AI video](https://dataliteracy.com/fusion_element/harnessing-generative-ai-video/) - What's included? Perpetual access to the Harnessing Generative AI on-demand course, offering: 6 course lessons with instructional videos and knowledge checks 6 instructional videos A total of ~90 minutes of video content 6 lesson quizzes with a total of 60 questions Links to helpful resources for further learning Sorry, your browser doesn't support embedded videos.
- [HOW CAN I TAKE THE CLASS?](https://dataliteracy.com/fusion_element/how-can-i-take-the-class/) - How can I take the class? As an individual, you can enroll in the on-demand course, start right away, and progress through the lessons at your own pace right here on our site. If you are looking for training for your entire team, contact us for group rates. Our courses can be taken on-demand in our
- [What our students are saying](https://dataliteracy.com/fusion_element/what-our-students-are-saying/) - What our students are saying...
- [how can i take it?](https://dataliteracy.com/fusion_element/how-can-i-take-it/) - Do you want to be more creative in your data communication? Looking for specific creativity advice tailored for those in the data field? Chart Spark is a practical guide designed to change your perspective on creativity and integrate it into your work.
- [who's it for](https://dataliteracy.com/fusion_element/whos-it-for/) - Do you want to be more creative in your data communication? Looking for specific creativity advice tailored for those in the data field? Chart Spark is a practical guide designed to change your perspective on creativity and integrate it into your work.
- [what's covered](https://dataliteracy.com/fusion_element/whats-covered/) - Do you want to be more creative in your data communication? Looking for specific creativity advice tailored for those in the data field? Chart Spark is a practical guide designed to change your perspective on creativity and integrate it into your work.
- [what's included](https://dataliteracy.com/fusion_element/whats-included/) - What's Included? A digital copy of Data Literacy Fundamentals by best-selling data author Ben Jones Perpetual access to the Data Literacy Fundamentals on-demand offering: 8 course lessons with instructional videos A total of 82 minutes of video content A pre-course and post-course knowledge check 8 lesson quizzes 1 Goal Tree template and other helpful resources
- [course section - what is included](https://dataliteracy.com/fusion_element/course-section-what-is-included/) - What's Included? A digital copy of Data Literacy Fundamentals by best-selling data author Ben Jones Perpetual access to the Data Literacy Fundamentals on-demand offering: 8 course lessons with instructional videos A total of 82 minutes of video content A pre-course and post-course knowledge check 8 lesson quizzes 1 Goal Tree template and other helpful resources
- [course section - what's included](https://dataliteracy.com/fusion_element/course-section-whats-included/) - What’s Covered in the Training? Each lesson is neatly divided into three sections for ease of learning: Core Concepts, Real-World Examples, Practical Applications. We'll cover: How to turn data into wisdom Two systems of human thinking Areas of life where data matters Four data scale types Different types of data analysis Ways to display data
- [course section - who's it for](https://dataliteracy.com/fusion_element/course-section-whos-it-for/) - Who Is It For? Data Literacy Fundamentals is for anyone who is just getting started with data and who wants to feel more confident in their understanding of what data is, what it isn't, and what it's used for. Even those who are "data-phobic" are welcomed to sign up and take this class. No experience
- [Chart Spark](https://dataliteracy.com/fusion_element/chart-spark/) - Chart Spark: Innovative Thinking in Data Communication If you work with data or data visualization and want to communicate with more impact, this course is for you. It's a practical guide designed to change your perspective on creativity and integrate it into your work. Each lessson includes actionable prompts designed to trigger your creativity, rather
- [NEW Hero Banner Mobile](https://dataliteracy.com/fusion_element/new-hero-banner-mobile/) - NEW BOOK! 📊⚡Chart Spark by Alli Torban is Now Available×DATA LITERACYFOR ORGANIZATIONS AND INDIVIDUALS What are you looking for? TRAINING MAKE A TEAM PLANBUY A COURSEASSESSMENTS ASSESS YOUR TEAMEARN A BADGESUPPORT FIND CONSULTINGWORK WITH A COACH
- [NEW Hero Banner](https://dataliteracy.com/fusion_element/new-hero-banner/) - NEW BOOK! 📊⚡Chart Spark by Alli Torban is Now Available×Sorry, your browser doesn't support embedded videos.DATA LITERACYFOR ORGANIZATIONS AND INDIVIDUALS What are you looking for? TRAINING MAKE A TEAM PLANBUY A COURSEASSESSMENTS ASSESS YOUR TEAMEARN A BADGESUPPORT FIND CONSULTINGWORK WITH A COACH
- [CTA - 17 Key Traits](https://dataliteracy.com/fusion_element/cta-17-key-traits/)
- [My Account - Old](https://dataliteracy.com/fusion_element/my-account-old/) - Your Badges Please login to the site to view the earned achievements.
- [CTA - ADP Checklist](https://dataliteracy.com/fusion_element/cta-adp-checklist/)
- [Ben’s Bio on About pg](https://dataliteracy.com/fusion_element/bens-bio-on-about-pg/) - About. INSPIRED BY DATA. Ben Jones is the founder and CEO of Data Literacy, LLC, a training and education company that's on a mission to help people learn the language of data. Ben is a highly experienced and passionate instructor, having taught data to thousands in a corporate training environment as well as in academic
- [skills bars](https://dataliteracy.com/fusion_element/skills-bars/) - DATA VISUALIZATION DESIGN 95%
- [Training Hero Banner Mobile](https://dataliteracy.com/fusion_element/training-hero-banner-mobile/) - Data Literacy: Level 1 How to Read and Interpret Data Visualizations FOR INDIVIDUALSFOR TEAMS
- [Training Hero Banner](https://dataliteracy.com/fusion_element/training-hero-banner/) - Data Literacy: Level 1 How to Read and Interpret Data Visualizations FOR INDIVIDUALSFOR TEAMS
- [Modal Customize Form](https://dataliteracy.com/fusion_element/modal-customize-form/) - ×Let’s Customize! Thank you for your interest in Data Literacy! We believe that everyone can become more fluent in the language of data. To help you get there, we’d like to understand some key aspects of your present situation. Please take a moment to fill out this form and we’ll reach out to you via
- [Slant 3/3/19](https://dataliteracy.com/fusion_element/slant-3-3-19/)
- [Advisory Board 3/3/19](https://dataliteracy.com/fusion_element/advisory-board-3-3-19/) - ADVISORY BOARD Contributors & Advisors Alberto Cairo Position: Knight Chair at the University of Miami Website: http://www.thefunctionalart.com/ Read Bio > Andy Cotgreave Position: Technical Evangelist, Tableau Software Website: http://gravyanecdote.com/ Read Bio > Cheryl Phillips Position: Lorry I. Lokey Visiting Professor in Professional Journalism at Stanford University Website: https://comm.stanford.edu/faculty-phillips/ Read More > Giorgia Lupi Position: Information Designer, Co-Founder
- [Team Bio 2/26/19](https://dataliteracy.com/fusion_element/team-bio-2-26-19/) - Ben Jones Lorem ipsum dolor sit amet, qui at tation aeterno complectitur. An duo libris feugiat fastidii, pri sonet semper at. Essent pertinacia sea in. In nam enim virtute praesent. An ius vero prompta vituperatoribus.Lorem ipsum dolor sit amet, qui at tation aeterno complectitur. An duo libris feugiat fastidii, pri sonet semper at. Essent pertinacia
- [slant](https://dataliteracy.com/fusion_element/slant/)
- [Line Under](https://dataliteracy.com/fusion_element/line-under/)
- [Team 2/23/19](https://dataliteracy.com/fusion_element/team-2-23-19/) - OUR TEAM Contributors & Advisors Ben Jones Founder Ben's Bio > Alberto Cairo Position: Knight Chair at the University of Miami Education: UNC Chapel Hill Areas of Expertise: World renowned data educator and consultant Best-selling author and popular blogger on the subject of data visualization and analytics Experienced data journalist and infographic designer Why Chosen:
- [Middle Down Triangle](https://dataliteracy.com/fusion_element/middle-down-triangle/)
- [home modal button - video](https://dataliteracy.com/fusion_element/home-modal-button-video/) - How it works
- [Why Us list 2/21/19](https://dataliteracy.com/fusion_element/why-us-list-2-21-19/) - Why Us? Get the data skills your school didn't give you Go beyond training in just one single tool Clean and structure data in a more efficient way Grasp the underlying statistical concepts Understand what the numbers actually mean Build confidence in your analysis & insights Become a more effective data storyteller Help your team
- [modal pop up](https://dataliteracy.com/fusion_element/modal-pop-up/) - ×Ask One of Our Experts! [contact-form-7 id="661" title="Ask Expert"] Close
- [BOOK LINK](https://dataliteracy.com/fusion_element/book-link/) - DOWNLOAD E-BOOK
- [BOOK BUTTON](https://dataliteracy.com/fusion_element/book-button/) - DOWNLOAD E-BOOK
- [BOOK TEXT](https://dataliteracy.com/fusion_element/book-text/) - 17 KEY Traits of Data Literacy
- [headshot w/ link to bio](https://dataliteracy.com/fusion_element/headshot-w-link-to-bio/) - Ben Jones Founder Ben's Bio >
- [line above text](https://dataliteracy.com/fusion_element/line-above-text/)
- [Home Hero - 12/31/18](https://dataliteracy.com/fusion_element/home-hero-12-31-18/) - HELP PEOPLE LEARN THE LANGUAGE OF DATA Instructor-led online - Onsite - Custom Training How it works×How it works
- [Latest Articles - 9/28/18](https://dataliteracy.com/fusion_element/latest-articles-9-28-18/) - LEADING MINDS. LATEST PUBLICATIONS. Ben Jones2026-06-08T08:02:21-07:00Now Available: The Adaptive OrganizationBen Jones2026-06-08T08:02:21-07:00June 8th, 2026|Comments Off on Now Available: The Adaptive Organization Now Available: The Adaptive Organization Patrick McGarry's The Adaptive Organization: Leading Change in the AI Era [...]Ben Jones2026-05-21T12:15:49-07:00New Book: The Adaptive Organization by Patrick McGarryBen Jones2026-05-21T12:15:49-07:00May 21st, 2026|Comments Off on New Book: The Adaptive Organization
- [Case Studies Carousel – 9/20/18](https://dataliteracy.com/fusion_element/case-studies-carousel-9-20-18/) - CASE STUDIES
- [Practice Areas - 9/20/18](https://dataliteracy.com/fusion_element/practice-areas-9-20-18/) - What we do Sexual Harassment and Sexual Abuse When those with authority abuse their power for sexual gain, the victims have a right to expect justice and monetary compensation from the institutions that vested the perpetrator with authority. Workplace Discrimination and Harassment You have a right to a discrimination-free work environment. Retaliation and Whistleblowing Employers
- [Reviews - 9/13/18](https://dataliteracy.com/fusion_element/reviews-9-13-18/) - “Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nvelit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.”Joe Doe • Avvo Review“Lorem ipsum
- [Flip Box w/ Map - 9/12/18](https://dataliteracy.com/fusion_element/flip-box-w-map-9-12-18/) - LONDON OFFICE12345 North Main Street, New York, NY 555555 Phone: 1.800.555.6789 Email: support@example.com Web: Themefusion.com NEW YORK OFFICE12345 North Main Street, New York, NY 555555 Phone: 1.800.555.6789 Email: support@example.com Web: Themefusion.com SAN FRANCISCO12345 North Main Street, New York, NY 555555 Phone: 1.800.555.6789 Email: support@example.com Web: Themefusion.com
- [Review slider - 9/13/18](https://dataliteracy.com/fusion_element/review-slider-9-13-18/) - “Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nvelit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.”Joe Doe • Avvo Review“Lorem ipsum
- [Results - 9/12/18](https://dataliteracy.com/fusion_element/results-9-12-18/) - Results Matter! We’re proud of the verdicts, judgments, and settlements we’ve won on behalf of our clients $14+ million in cases won America's Premier Attorneys™
- [Bio Social 9/12/18](https://dataliteracy.com/fusion_element/bio-social-9-12-18/)
- [Latest 4 article grid - 9/12/18](https://dataliteracy.com/fusion_element/latest-4-article-grid-9-12-18/) - LEADING MINDS. LATEST PUBLICATIONS. Ben Jones2026-06-08T08:02:21-07:00Now Available: The Adaptive OrganizationBen Jones2026-06-08T08:02:21-07:00June 8th, 2026|Comments Off on Now Available: The Adaptive Organization Now Available: The Adaptive Organization Patrick McGarry's The Adaptive Organization: Leading Change in the AI Era [...]Ben Jones2026-05-21T12:15:49-07:00New Book: The Adaptive Organization by Patrick McGarryBen Jones2026-05-21T12:15:49-07:00May 21st, 2026|Comments Off on New Book: The Adaptive Organization
- [CTA - Lets work together](https://dataliteracy.com/fusion_element/cta-lets-work-together/) - LET'S WORK TOGETHER We work as a single united team with market leading firms around the world and give our clients the highest quality advice possible. MAKE ENQUIRY
- [Home > Results](https://dataliteracy.com/fusion_element/home-results/) - Results Matter! We’re proud of the verdicts, judgments, and settlements we’ve won on behalf of our clients $14+ million in cases won America's Premier Attorneys™

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- [podcasts](https://dataliteracy.com/tag/podcasts/)
- [data](https://dataliteracy.com/tag/data/)
- [thought leadership](https://dataliteracy.com/tag/thought-leadership/)
- [course](https://dataliteracy.com/tag/course/)
- [custom](https://dataliteracy.com/tag/custom/)
- [change](https://dataliteracy.com/tag/change/)
- [new book](https://dataliteracy.com/tag/new-book/)

## Product categories

- [Uncategorized](https://dataliteracy.com/product-category/uncategorized/)
- [Online Course](https://dataliteracy.com/product-category/online-course/)
- [Books](https://dataliteracy.com/product-category/books/)
- [Assessments](https://dataliteracy.com/product-category/assessments/)
- [Transformational Workshop](https://dataliteracy.com/product-category/transformational-workshop/)
- [Subscriptions](https://dataliteracy.com/product-category/subscriptions/)

## Course Categories

- [Core Courses](https://dataliteracy.com/course-category/core-courses/)
- [Accent Courses](https://dataliteracy.com/course-category/accent-courses/)
- [Assessments](https://dataliteracy.com/course-category/assessments/)
- [Knowledge Checks](https://dataliteracy.com/course-category/knowledge-checks/)

## Course Tags

- [ChatGPT](https://dataliteracy.com/course-tag/chatgpt/)
- [AI](https://dataliteracy.com/course-tag/ai/)
- [LLM](https://dataliteracy.com/course-tag/llm/)

## Lesson Categories

- [Level 1](https://dataliteracy.com/lesson-category/level-1/)