CSE 512 Data Visualization

The world is awash with increasing amounts of data, and we must keep afloat with our relatively constant perceptual and cognitive abilities. Visualization provides one means of combating information overload, as a well-designed visual encoding can supplant cognitive calculations with simpler perceptual inferences and improve comprehension, memory, and decision making. Furthermore, visual representations may help engage more diverse audiences in the process of analytic thinking.

In this course we will study techniques for creating effective visualizations based on principles from graphic design, perceptual psychology, and cognitive science. The course is targeted both towards students interested in using visualization in their own work, as well as students interested in building better visualization tools and systems.

In addition to class discussions, students will complete visualization design and data analysis assignments, as well as a final project. Students will share the results of their final project as both an interactive website and a video presentation.

This course is designed to provide students with the foundations necessary for understanding and extending the current state of the art in data visualization. By the end of the course, students will have gained:

• An understanding of key visualization techniques and theory, including data models, graphical perception and methods for visual encoding and interaction.
• Exposure to a number of common data domains and corresponding analysis tasks, including multivariate data, geo-spatial data, and networks.
• Practical experience building and evaluating visualization systems.
• The ability to read and discuss research papers from the visualization literature.

• The Visual Display of Quantitative Information, E. Tufte. Graphics Press, 2001.
• Envisioning Information, E. Tufte. Graphics Press, 1990.
• Vega-Lite Visualization Notebook Curriculum. Jeffrey Heer, Dominik Moritz, Jake VanderPlas, and Brock Craft.
• Optional: Interactive Data Visualization for the Web, 2nd Edition. Scott Murray, O'Reilly Press.
  • To read online, use the "O'Reilly (Formerly Safari)" link on the UW library page.
  • Code examples available on GitHub.