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 and algorithms for creating effective visualizations based on principles from graphic design, perceptual psychology, and cognitive science. Students will learn how to design and build interactive visualizations for the web, using the Vega-Lite and D3.js (Data-Driven Documents) frameworks.

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

• 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.
• Vega-Lite Visualization Notebook Curriculum. Jeffrey Heer, Dominik Moritz, Jake VanderPlas, and Brock Craft.

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 applications.

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: Expository Visualization 10%
• Assignment 2: Deceptive Visualization 15%
• Assignment 2: Peer Review 5%
• Assignment 3: Interactive Visualization 20%
• Assignment 3: Peer Review 5%
• Final Project 35%

Policies

Late Policy: You have two (2) total late days that you can apply as needed to turn in an individual assignment (A1, A2, Peer Reviews) after the due date without penalty. For example, you can submit A1 and A2 one day late, or submit just the A2 peer review two days late. Each project team also has an additional late day for A3. No late days are given for final project milestones. Beyond late days, we will deduct 10% for each day an assignment is late. Please contact the instructors on Ed Discussion prior to a deadline if you intend to apply your late days or if you would like to request additional accommodations.

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 (or if you are unable, to watch the recordings) and read the readings. Each lecture will assume that you have read and are ready to discuss the day's readings. Tuesdays are devoted to lectures and Thursdays to in-class exercises, which are included in participation grading. In-class activities are considered in terms of best effort rather than completion (in other words, did you try to complete the in-class exercise?).

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.

UPDATE 01/04/2023: The quizzes are technically graded but from a participation perspective; you can take them as many times as you want to get a perfect score. The "grading" is more so we can use Canvas to auto-grade it, rather than manually input a score for completion/participation. It also helps so students can see the correct answers.

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 cse442@cs or discuss it at office hours.