About

Visualization is a crucial cornerstone of data science and a skill that is increasingly required in many other areas in science, engineering, business, and journalism. The scale and complexity of data that people must understand and reason about has exploded in nearly every field, so many students want to learn how to present data visually and gain fluency in visual data analysis. Visualization is suitable when there is a need to augment human capabilities rather than simply replace people with computational decision-making methods. Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively. In this course, you will learn about how to design and critique visualization systems through reading research papers and textbook chapters, working through deep-dive active learning exercises in small groups in class, and creating a substantial final project.

The course has a mostly flipped format, and is taught in a 3-hour block once a week. In the first 10 weeks students read textbook chapters and research papers and contribute to the asynchronous discussion about them before class. Students must post about each of three readings early in the week, and then respond to at least one post from another student within two more days. During class, I follow up the async discussion with additional commentary by answering selected topics in more depth through mini-lectures. Six of the class sessions are used for deep-dive active learning exercises in small groups, interleaved with reportbacks framed by critique and response from the instructor. No tooling is taught in the course; students are free to use whatever tools best match their strengths and interests.

The final project, conducted over the last 9 weeks, is typically done in small self-chosen student teams, with solo projects allowed by permission of instructor for well-justified requests. Four class sessions are devoted to project work, including a pitch session before teams are formed, pre-proposal meetings each team and me, pairwise peer reviews between teams, and post-update meetings with each team and me. Project work concludes with final presentations, then teams have a few more days in which to finish their final reports.

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