vis teaser

The amount and complexity of information produced in science, engineering, business, and everyday human activity is increasing at staggering rates. The goal of this course is to expose you to visual representation methods and techniques that increase the understanding of complex data. Visualization for data discovery and communication is an important part of the data science pipeline. Good visualizations not only present a visual interpretation of data, but do so by improving comprehension, communication, and decision making.

In this course you will learn about the fundamentals of perception, the theory of visualization, good design practices for visualization, and how to develop your own web-based visualizations using HTML5, CSS, JavaScript, SVG, and D3.

The course begins by bootstrapping your web development skills, moves on to fundamentals of perception, introduces data types you will encounter, and then focuses on visualization techniques and methods for a broad range of data types. An integral component of the course are regular design critiques and redesigns that will hone your skills in understanding, critiquing and developing visualization techniques.

The course is offered in the fall term 2017 at the University of Utah in two variants: CS-5630 for undergraduates and CS-6630 for graduate students, with a special section of CS-6630 (002) designated for data certificate students. Classes start on Tuesday, August 22. All classes are streamed live and archived online.

New in 2017 - “Visualization for Data Science” replaces “Visualization”

The course has been renamed to “Visualization for Data Science”, and the content will reflect that new emphasis. We will focus on the data types that are commonly encountered in the data science process, such as tables, graphs, geospatial data, and text.

We will no longer cover “scientific data”, i.e., spatila data in this course. A new, complementary course - Visualization for Scientific Data (CS-5635/CS-6635) - that focuses on the visualization of spatial data (e.g., grid-based data from simulations and scanning devices) is offered in the spring.


Alexander Lex,,
Phone: (801) 585-0327
Office: WEB 3887

Teaching Assistants

Carolina Nobre, PhD student, Visualization / Computer Science
Pranav Dommata, MS student, Visualization / Computer Science
Trang Tran, PhD student, Computer Science


Lectures: Tuesday and Thursday 2:00-3:20 pm, L101 WEB
Labs: By announcement on Tuesday, 6:00-7:30 pm, L110 WEB
Live Video: YouTube Channel
Archived Video: YouTube Playlist
Grades: Canvas
Discussion: Slack - please join with your address and use your real name.

Office Hours

For up to date office hours and cancellations, time or location changes please refer to our Google Calendar.

Alexander Lex: Wed, 2:00-3:00, WEB 3887
Carolina Nobre: Thu, 3:30-5:30, MEB 3115
Trang Tran: Tue, 12:00-2:00, MEB 3115
Pranav Dommata: Wed, 12:00-2:00, MEB 3115

Previous Versions of this Class

If you want to look ahead in the class (in particular regarding slides) you can browse last year’s page here:
Utah CS-5630/CS5660 2016
Utah CS-5630/CS5660 2015

This class is based on two different courses:

Harvard CS 171 2015
Instructor: Alexander Lex
Video Archive

Utah CS-5630/CS5660 2014
Instructor: Miriah Meyer