CSE442 Data Visualization (Spring 2017)

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 D3.js (Data-Driven Documents) framework.

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 short presentation.

Students presented their projects on June 5 at the Paul G. Allen Center.
Experience the final visualization projects online!


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:

Schedule & Readings

Week 1

Week 2

Thu 4/6 Web Programming (JavaScript, SVG, CSS) Tutorial Material
  • 4:30-5:50pm, PAA A118

Week 3

Tue 4/11 Visualization Tools Slides
Thu 4/13 Interaction Techniques Slides
Assigned: Final Project Proposal (Due: Tue 4/18, 5pm)
Thu 4/13 Introduction to D3.js Material
  • 4:30-5:50pm, PAA A118

Week 4

Week 5

Tue 4/25 Maps Slides

Week 6

Thu 5/4 Prototype Demos Slides
Assigned: Peer Evaluation (Due: Wed 5/10, 5:00pm)

Week 7

Tue 5/9 Peer Evaluation (No Lecture)

Week 8

Week 9

Week 10

Thu 6/1 Final Project Presentations Slides

Finals Week



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. Links to the Canvas discussion for each week will be posted on the schedule above.

Good comments typically exhibit one or more of the following:


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


Questions should be posted on Canvas. If you have a private question, email the instructors at cse442@cs or come to office hours.