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

• Published in: Journal of statistics and data science education pp. 1 - 25
• Main Authors: Branson, Zach, Paz Parra, Monica, Yurko, Ronald
• Format: Journal Article
• Language: English
• Published: Taylor & Francis Group 21.07.2025
• Subjects: course design; course survey; graphics; inference; statistics education
• ISSN: 2693-9169; 2693-9169
• DOI: 10.1080/26939169.2025.2537049

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.