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.

Textbooks

• 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.

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, geo-spatial data, and networks.
• 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. Please contact the instructors well in advance to request extensions if needed.

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.

Religious Accommodation: Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. The UW’s policy, including more information about how to request an accommodation, is available here: Religious Accommodations Policy. Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form.

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-lecture activities (as is feasible) and engagement on the course discussion site (Ed). Up through week 8, all enrolled students are required to submit at least 1 substantive discussion post per week related to the course readings or lecture material. Each student also 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

In addition, we will post short quizzes to reinforce important concepts. The quizzes are not graded (your score on a quiz will not affect your course grade), but you are required to complete the quiz as part of your course participation.

Resources

See the resources page for visualization tools, related web sites, and software development tips.

Q&A

Questions should be posted on the course discussion site (Ed). If you have a private question, please email the instructors at cse512@cs or discuss it at office hours.