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

Week 11

Assignments

• Class Participation 10%
• Assignment 1: Expository Visualization 10%
• Assignment 2: Deceptive Visualization 10%
• Assignment 2: Peer Review 5%
• Assignment 3: Interactive Visualization 15%
• Assignment 3: Peer Review 5%
• Quiz 1 5%
• Quiz 2 5%
• Final Project 35%

Activities, Learning Assessments & Expectations for Students

Important Announcements: We will send all course announcements through Ed, including corresponding emails. You should make sure that you do not miss these messages. You are responsible for checking your email and Ed with regular frequency.

Readings: To contribute to lively and insightful discussions, we each need to do our part to be prepared. Students are expected to complete assigned readings prior to lecture. All readings are listed in the course schedule.

Lectures & In-Class Activities: It is important to attend lectures and read the readings. In class we will assume that you have read the day's readings and are ready to engage in the day's activities. In-class lectures will be in person and recorded. For in-class activity days, lectures may be pre-recorded and shared ahead of time. Students may miss one activity day without penalty to their course participation grade. In addition, students may have excused absences due to illness, work-related travel, or other issues; please reach out to the instructor as needed.

Assignments: There will be three assignments to help prepare you for the final project. These assignments are designed to give students experience in creating and evaluating visualization tools. See the course calendar for assignment dates.

Final Project: A team-based (1-4 person) final project is due at the end of the course, with periodic milestones. The goal of the final project is to identify a new, interesting and challenging visualization problem, and to apply the techniques and skills learned in class to address this problem. Students are welcome to choose a topic area of interest (including one's own research) for the final project.

Quizzes: There will be two in-class quizzes happening during weeks 4 and 7 of class. You are expected to come to class on these days. If you are unable to make class on these days due to sickness, work-related travel, etc, please contact the course staff so we can make alternative arrangements. Each quiz will cover content from the prior three weeks of lectures, activities, required readings, and assignments. Tutorial content is in-scope only insofar as it overlaps with lecture/activities/readings/assignments. Quizzes will contain multiple choice and short answer questions, and will take 20 minutes of class time.

Please feel free to reach out about personal, academic, or intellectual concerns. However, please consult the syllabus and Canvas first for logistical questions, such as when an assignment is due or how much an assignment is worth in terms of grading.

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. No late days are given for A3 or 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.

Generative AI Policy: All visualization designs in this course should be the result of your own design thinking and deliberation. If you are inspired by third party works it is fine to borrow design ideas and even example code; just be sure to cite such sources in your write-up and indicate how and to what extent you drew upon each source. If you use AI for code completion help (as opposed to design assistance), you must also document how and to what extent you used it. For assignment write-ups, you may not use generative AI: you must write your own. We will not grade you on grammar, spelling, or fluency, and we want to read your rationales in your own voice. If we detect likely AI-generated write-ups, we will investigate and contact you for follow-up.

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.

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.