This week we have a discussion starter on the Stanford “Datavisualization and the Modern Imagination” exhibit at Stanford to help us think through the long history of data visualization and interpreting visualizations.
This week will look like a lot of reading in the “Fundamentals of Data Visualization” book, but many of the sections are quite short; be sure to pay specific attention to what not to read since I have not assigned particular sections. In each chapter, there are multiple sub-sections, and we are not reading all sub sections.
Much of the Fundamentals of Data Visualization is about the technical description of different kinds of charts. Like many of you observed about Python, this will involve learning how words that you might be familiar with in other contexts have specific technical meanings that can trip you up. Try not to get hung up on specific details (what’s a logarithmic scale?? Do I need to remember how to do square roots???), but rather focus on Wilke’s argument about using charts that suit the data you’re working with and how layout affects readability. Keep in mind the different kinds of data we thought about in our data critique and data cleaning assignments, and that the kind of data you have will affect the kinds of visualizations you can do with it. Fundamentals of Data Visualization will be particularly helpful to return to once you’re a week or two into your final project.
Module 4 was our last required Python assignment, but as many of you observed in your posts this week, slowing down and looking at the details is a good skill to have. This will also be the case in Module 5 while we work with Tableau Public, the last piece of software we’re downloading. (Please note that Tableau and Tableau Public are different–Tableau Public is free and regular Tableau is not!) I have deliberately waited until fairly late in the semester to introduce Tableau because it is deceptively friendly, but it will benefit from the kind of attention to detail you have been working on in previous modules. Order and data type matters in Tableau even though you will not be doing any writing of code yourself. I also strongly suggest watching
the Tableau training videos 1: Overview, 11: Creating Your First Chart, and 20: Publishing at a minimum, which are about 15 minutes of video together, after you’ve downloaded the program. The videos will orient you to the basic interface and the assignment will walk you through making basic chart types covered in the Fundamentals of Data Visualization, so it may be helpful to refer back to the text for those chart types as you work through the assignment.
It may not feel like it now, but we are coming in to the final stretch of new material. There are optional javascript/Observable assignments this week and in module 7, and there is an optional python assignment in module 7. That means that unless you want to do more coding, you don’t have to. A brief overview of the next few weeks:
- Module 5 (this week): introduce Tableau and data visualization
- Module 6: no reading — your only assignment is to post about your final project plans
- Module 7: learn to read network analysis, with optional python analysis and javascript visualization assignments
- Module 8: learn some mapping techniques
Module 6 is more or less our midterm spring break. I will be available next week, but your only task is to take a break and think about your final project plans. Modules 7 and 8 will introduce networking and mapping as analysis techniques, and after that we’ll transition into final project work. Start thinking about the kind of work you’ve enjoyed or found most compelling, and what you want to spend the last month of the semester doing.
I will be available on Zoom during Monday office hours 2-3PM, our Wednesday afternoon scheduled meeting time, and throughout the week on Slack and email to help troubleshoot the assignments.