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!

Textbook

• Interactive Data Visualization for the Web. Scott Murray, O'Reilly Press. (Free Online!) Code examples available on GitHub. We will be using updated examples for D3 v4.

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, networks, text and cartography.
• Practical experience building and evaluating visualization systems.

Schedule & Readings

Week 1

Week 2

Week 3

Week 4

Week 5

Week 6

Week 7

Week 8

Week 9

Week 10

Finals Week

Assignments

• Class Participation 10%
• Assignment 1: Visualization Design 10%
• Assignment 2: Exploratory Data Analysis 15%
• Final Project 65%

Policies

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:

• 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

Resources

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

Q&A

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