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 through both interactive demos and a poster session.

There are no prerequisites for the class and the class is open to graduate students as well as advanced undergraduates (by permission of instructor). Basic working knowledge of, or willingness to learn, graphics/visualization tools (e.g., D3, Vega, HTML5, OpenGL, etc) and data analysis tools (e.g., R, Python, Excel, Matlab) will be useful.

Final Projects will be presented in the Paul G. Allen Center at the University of Washington on Thursday May 31, 12-2pm.

Textbooks

• The Visual Display of Quantitative Information, E. Tufte. Graphics Press, 2001.
• Envisioning Information, E. Tufte. Graphics Press, 1990.
• Optional: Interactive Data Visualization for the Web, 2nd Edition. Scott Murray, O'Reilly Press. (Read Online!) Code examples available on GitHub.

Learning Goals & Objectives

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, networks, text and cartography.
• Practical experience building and evaluating visualization systems.
• The ability to read and discuss research papers from the visualization literature.

Schedule & Readings

Week 1

Week 2

Week 3

Week 4

Week 5

Week 6

Week 7

Week 8

Week 9

Week 10

Assignments

• Class Participation 10%
• Assignment 1: Visualization Design 10%
• Assignment 2: Exploratory Data Analysis 15%
• Assignment 3: Interactive Visualization 25%
• Final Project 40%

Policies

Late Policy: We will deduct 10% for each day an assignment is late.

Plagiarism Policy: Assignments should consist primarily of original work. Building off of others' work—including 3rd party libraries, public source code examples, and design ideas—is acceptable and in most cases encouraged. However, failure to cite such sources will result in score deductions proportional to the severity of the oversight.

Class Participation

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.

Class participation includes both in-class participation as well as participation in the discussion on Canvas. All enrolled students are required to submit at least 1 substantive discussion post per week related to the course readings. Each student has 1 pass for skipping comments.

Good comments typically exhibit one or more of the following:

• Critiques of arguments made in the papers
• Analysis of implications or future directions for work discussed in lecture or readings
• Clarification of some point or detail presented in the class
• Insightful questions about the readings or answers to other people's questions
• Links to web resources or examples that pertain to a lecture or reading

Resources

See the resources page for visualization tools, data sets, and related web sites.

Q&A

Questions should be posted on Canvas. If you have a private question, email the instructors at cse512@cs or come to office hours.