CSE442 Data Visualization (Fall 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.


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 0

Week 1

Tue 10/3 A1 Review & Tableau Tutorial Slides
Assigned: Assignment 2: Exploratory Data Analysis (Due: Mon 10/16)
Thu 10/5 Data & Image Models Slides

Week 2

Thu 10/12 Visual Encoding & Design Slides

Week 3

Thu 10/19 Interaction Techniques Slides
Thu 10/19 D3.js Tutorial, 5:00-6:20pm Sieg 134 Slides

Week 4

Week 5

Tue 10/31 Prototype Demos Slides
Assigned: Peer Evaluation 1 (Due: Mon 11/6)

Week 6

Thu 11/09 Maps Slides

Week 7

Week 8

Thu 11/23 Thanksgiving - No Class!

Week 9

Thu 11/28 Design Feedback Sessions
Assigned: Final Project Deliverables (Due: Wed 12/6)
Thu 11/30 Design Feedback Sessions

Week 10

Thu 12/7 Final Project Showcase



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. Up through week 7, 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 weeks 0-7 are included in 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.