For the course I teach on the design of data visualizations, I have used a large number of tools to create data visualizations. Past years have relied upon GUI-based tools, like Excel, Tableau, and (my absolutely favorite) Veusz. However, I have also used Python in the past. I really enjoyed running the course out of Python, because I think coding is a skill that is valuable for literally everyone, and I think that creating data visualizations is a fun and accessible way to learn to code. However, in the past I found I simply spent too much time helping people trouble shoot their code to justify this approach. Basically, the course turned into “intro to programming” rather than “data meets design.”

The course I teach on the design of data visualizations often includes students critiquing each others work, and revising their work in response to those critiques. There are a few reasons why I do this:

1. Providing critiques to others requires critical thinking about data visualizations. Thinking through a design and analyzing it is a great way to practice thinking about design.
2. The outcome is useful. Rather than just critiquing a static piece, critiquing a design that will change in the future means that the critiques provided have real impact.
3. It is useful to practice the (admittedly painful) practice of receiving critiques. When you put effort into a creation, and then have someone else respond to it, it is often hard to hear. But the more you practice this, the better you will be at parsing critiques and pulling out useful information. Thus, your own work will improve.

However, I also find that many students have not yet been expected to provide meaningful critiques to their peers, and so I have two write-ups that I provide. One on giving useful critiques and the other on receiving critiques. These are short pieces, but I hope they cover a bit of how to approach these aspects and how to avoid some common pitfalls.

The first draft of the articles discussing the different elements of plots is done! Next, I need to go back though and refine it and the articles on general design. The next goal is to refine them, and to create links between them.

After that, the next step is to start making pages for individual kinds of plots.

While it is still a fairly rough draft, the first round of getting the general design ideas down into the wiki is done! I will be working on the different parts of data visualizations next. Then, I will go back through and refine the writing and hopefully get much of the crosslinking in.

Most of the data visualizations I make in my line of work, and for this site, were done in Python, using the Plotly library. This year, the US Python conference, PyconUS, was held just down the road from State College, in Pittsburgh. So, I headed on over to check out the conference. Lots of interesting things, but there were a few talks that were relevant to data visualizations (typography, color, accessibility, and so on), but also I got to meet a few people from the Plotly team as well as the Marimo team! Good times all around.

I am currently at the 2025 Data Visualization in Science and Education Gordon Research Conference. This conference brings together a group of people who think deeply about how to design, create, and share data visualization, as well as how to evaluate the effectiveness of those visualizations. There are people building new tools—both traditional plotting software and software that visualizes data in virtual reality—as well as artists talking about basic design rules. It is a great conference.