In today's world, we are constantly bombarded with an increasing amount of data from various sources (e.g., stock price, COVID, weather data, etc.). It can be overwhelming to process and understand all of this information, especially when it is presented in raw, numerical form. This is where data visualization can help. Well-designed visualizations leverage human visual processing to improve comprehension, memory, inference, and decision-making. In this course, we will study techniques and algorithms for creating effective visualizations using principles from graphic design, visual art, 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. The material will be covered through lectures, readings, a number of assignments, and a final project.

It is important to attend the lectures and read the readings. Each lecture will assume that you have read and are ready to discuss the day's readings. Although the course is videotaped, because lectures often involve discussion, attendance at lecture is very strongly recommended for all students.


The goals of this course are to provide students with the foundations necessary for understanding and extending the current state of the art in visualization. By the end of the course, students will have:

  • 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 exploratory data analysis and network analysis.
  • Practical experience building and evaluating visualization systems using Vega-Lite and D3.js.
  • The ability to read and discuss research papers from the visualization literature.

Your best bet is to order them online. Please order soon. Readings will be assigned in the first week of class.

  1. The Visual Display of Quantitative Information (2nd Edition). E. Tufte. Graphics Press.
  2. Envisioning Information. E. Tufte. Graphics Press.
  3. Optional Textbook. Visualization Analysis and Design. Tamara Munzner. A K Peters Visualization Series. CRC Press.
  4. Optional Reference. Interactive Data Visualization for the Web (2nd Edition). Scott Murray. O'Reilly Press. [Read Online] [Code Examples on Github]