Data-Literacy.com has now become DataLiteracy.com. Please note the very significant, substantial, and meaningful removal of the hyphen. It turns out that the World Name Group, who contacted me about buying this domain, is, in fact, a legitimate company and not the spamming scammers that I first thought they were. Now we no longer have to explain that “datadatashliteracy.com” means “datahyphenliteracy.com” means “data-literacy.com.” Imagine my relief.
By the way, it turns out that my witty paraphrase at the top of this entry is perpetuating one of the most famous misquoted lines in movies. You can see the wonderful Wikipedia entry here.
I just finished recording two short courses for lynda.com:
- “Up and Running with R,” which provides a very brief introduction to data analysis in the free, open-source statistical programming language R (see r-project.org)
- “Projects for Processing,” a short, homework course to accompany the longer course “Interactive Data Visualization with Processing” that I did for them in the fall. Both of these focus on the free, open-source programming language Processing (see processing.org) that is designed for artists and designers, as well as data visualization.
Because these are short courses, they should both be released in just a few weeks. At that point, I’ll provide links to the table of contents for each course. Woo hoo!
I’ve just added a page with listings of courses that I have taught / am teaching / will teach on data related issues. There is a past course on generative art that I taught at the University of Utah, an upcoming course on data visualization at Utah Valley University, and my courses on SPSS and interactive data visualization for lynda.com. Take a look!
- Data scientist is new hot job for college grads (ksl.com)
- CFP: The History and Future of Data Visualization (ASECS 2013) (lmc.gatech.edu)
Okay, so that’s my extremely quick, hack-and-slash, DIY attempt at a logo, with sub-optimal color matching, but you get the idea. One of my biggest goals for my sabbatical was to create a “data lab” at UVU. Now that I’m back at school, I can’t wait to get that going. (Of course, I have to do some other things, like teach my classes and get my big Dance Loops project up and running.)
We have some opportunities for grants coming up that might work. In particular, the Engaged Learning in the Liberal Arts (ELLA) grants offered by UVU’s College of Humanities and Social Sciences, might work well. I’ve got about two weeks to get that together, so I better get cracking.
The basic idea is to offer courses that would help non-data scientists learn some of the skills of data science so they could mix it up with some of their own data. I hope to offer courses in statistical packages like SPSS and R, databases like MySQL, data visualization programs like Processing and Tableau Public, and introductions to topics like text mining, business intelligence, and digital humanities. Anyhow, I’ll get the proposal together and see how it goes. Whoo hoo!
I just finished spending a very sleepless week in Carpinteria, California, at the headquarters of lynda.com. My producer and I managed to miraculously finish an entire video course called “Interactive Data Visualization with Processing.” With any luck it will be released in a month or so. With any more luck, that will be before the official – i.e., non-alpha – version of Processing 2.0 comes out. (We recorded the entire course with 2.0 alpha 8, so things shouldn’t be too different.)
The SPSS course that I made for them last year went profitable right before I flew out. That is, it paid off its advance and I received my first direct deposit. Whoo hoo! I venture no guesses for the Processing course, but I’ll just be happy to have it out.
I’ll be sure to post the official announcement when it comes out.
Update: The course is published! See http://www.lynda.com/Processing-tutorials/Interactive-Data-Visualization-Processing/97578-2.html
When measuring variables in research, quantitative variables are a wonderful thing. Whenever you’re able to measure something – i.e., measure a quantity of something – as opposed to simply categorizing it, you have much richer, more statistically valuable information. (There are some important exceptions to this, but I’ll get to those another day.)
Quantitative variables can be broken down into two kinds: discrete quantitative variables and continuous quantitative variables. A discrete quantitative variable is one that can only take specific values. Examples include:
- The number of children in a family
- The number of times a person has been to Brazil
- The number of time someone has stuck calipers on your nose
Although this subcategory (i.e., discrete vs. continuous) doesn’t usually matter, it can make certain chart like histograms or scatterplots (which we will discuss later) look funny. There are, however, ways to deal with that, which we will also discuss later.
A continuous quantitative variable, on the other hand, is one that can theoretically be measured in infinitely small steps (what mathematicians call “an arbitrary level of precision”). Examples include:
- Distance between two people
- Time spent working on a puzzle
- The width of your nose in millimeters and fractions thereof
You can record distance, for example, as
- 4 feet
- 4.1 feet
- 4.121738502767485960… feet
There is a common error on this point that I have seen even in respectable statistics books. Many people call a variable continuous when they should call it quantitative. This is an error because a quantitative variable can also be discrete. In addition, people often use the term discrete to mean categorical, which is an error because discrete variables can also be quantitative. Ay yi yi… Data fans of the world: Please get it straight. I’ll thank you now.
I finished last week with a post on Edward Tufte (there he is, right above), so maybe it’s fitting to start this week with him again. I’ll have a lot more to say about his work on visual graphics in the future, but I thought I’d throw in a little about his extreme disdain for PowerPoint. In fact, he wrote a short piece in Wired that was simply entitled “PowerPoint is Evil.”
For example, he has published a little booklet (28 pages) entitled The Cognitive Style of PowerPoint: Pitching out Corrupts Within. Gotta love the thought cloud on the bottom right: “There’s no bullet list like Stalin’s bullet list!”
Anyhow, his objections can be characterized thusly:
- PowerPoint slides allow for very little text, so very little content
- The sequential nature of the presentation makes it hard to compare and evaluate information
- People tend to spend more time on the visual design of their slides then they do on informative content
- The resulting presentations tend to resemble sales pitches more than attempts at communication
Well, make of that what you will. But I have to admit I love the morbid humor that his diatribe has spawned:
So present wisely.
- PowerPoint, Memory and Visual Storytelling (customerthink.com)
- New Book Combats the Cost of Poor PowerPoint Presentations (prweb.com)