The Landscape of College-level Data Visualization Courses, and the Benefits of Incorporating Statistical Thinking

TL;DR

This paper surveys college-level data visualization courses in the US, highlighting their focus areas and lack of statistical inference content, and proposes teaching principles to integrate statistical thinking into visualization education.

Contribution

It provides a comprehensive survey of visualization courses and introduces principles for incorporating statistical inference into visualization teaching.

Findings

Most courses are not taught by statistics departments.

Courses focus on storytelling and aesthetics rather than inference.

Survey dataset enables further exploration of course diversity.

Abstract

Data visualization is a core part of statistical practice and is ubiquitous in many fields. Although there are numerous books on data visualization, instructors in statistics and data science may be unsure how to teach data visualization, because it is such a broad discipline. To give guidance on teaching data visualization from a statistical perspective, we make two contributions. First, we conduct a survey of data visualization courses at top colleges and universities in the United States, in order to understand the landscape of data visualization courses. We find that most courses are not taught by statistics and data science departments and do not focus on statistical topics, especially those related to inference. Instead, most courses focus on visual storytelling, aesthetic design, dashboard design, and other topics specialized for other disciplines. Second, we outline three teaching principles for incorporating statistical inference in data visualization courses, and provide several examples that demonstrate how to follow these principles. The dataset from our survey allows others to explore the diversity of data visualization courses, and our teaching principles give guidance for encouraging statistical thinking when teaching data visualization.