Science of Data Visualization

Catalog description

Data visualization is the representation of information and data, through visual graphics like charts, plots, and even animations. A ‘designer’ creates and uses data visualizations to communicate complex data relationships in ways that are meant to be easy to understand and aesthetically pleasing. As scientists, we are all designers in this sense – we create data visualizations to illustrate our research findings, and use them to communicate about our research in publications and presentations. Effective data visualizations convey the information intended by the designer, in ways that are easy to understand in a short amount of time, and are enjoyable to look at. In this graduate-level course, we will explore the perceptual and cognitive factors that inform visual design when producing effective data visualizations, with an emphasis on color perception, color cognition, spatial perception, and visual attention. Students will read journal articles and book chapters about basic perceptual and cognitive principles in preparation for class discussions related to designing data visualizations. In addition to course readings, we will also critically examine existing examples of data visualizations to determine the elements that make them more/less effective. Students will also get extensive experience in producing data visualizations leveraging the principles learned through the course, and practice presenting their data visualizations. This course is designed to be an advanced elective for graduate students in COS. Students should be prepared to use computational software of their choice to work with data and produce visualizations (e.g., MATLAB, Python, R, etc.), and should be prepared to read and interpret journal papers covering visual perception and cognition topics.