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.

This course introduces the principles, methods, and techniques for applied data visualization. It is designed for students who want to learn how to effectively communicate data, e.g., in their research or work, by using both interactive tools and scripting.

We will explore aspects of visualization related to tabular data, networks, text, and maps. The course balances fundamental aspects of data visualization (perception, design, visualization techniques, etc.) and practical hands-on skills, such as how to create figures (e.g., for scientific or journalistic publications) and interactive visualizations, (e.g., for data-driven discovery).

We will create visualizations with GUI tools, such as Tableau and PowerBI, but also using programming in Python within computational notebooks and libraries such as Matplotlib, Seaborn, and Altair. Throughout the course, we will continue to analyze, critique, and redesign visualizations. After completing this course, you will be able to confidently identify which visualization design is suitable for a dataset and analysis question, and produce the visualization yourself.

Students who are interested in becoming visualization researchers, or who want to develop highly custom and interactive visualizations, or who are enrolled in a program in the Kahlert School of Computing should consider taking CS 6630 – Visualization for Data Science instead (the CS graduate research course). The main different between CS 6630 and this course is that we will focus on practical applicability with the programming languages and means that are easily accessible to non-CS majors.

The course is offered in the fall term 2024 at the University of Utah as COMP-5960.

Instructor

Alexander Lex
E-Mail: alex@sci.utah.edu, please do not send me canvas messages.
Phone: (801) 585-0327
Office: WEB 3887

The course is taught by Alexander Lex, an experienced visualization professional and faculty member of the University of Utah. Alex runs the Visualization Design Lab, a visualization research group at the SCI Institute, where he develops novel visualization solutions for scientists in both academic and industry settings.

Alex has taught visualization courses for more than ten years, including at Utah, Harvard, and for various corporations. Alex has developed many widely used open source visualization techniques that have been adopted, e.g., by Microsoft in their PowerBI platform or are in use both in academic and commercial research labs. He has won numerous awards, including multiple best paper awards or honorable mentions at visualization conferences and a best dissertation award from his alma mater.

Alex is also a co-founder of datavisyn, a data visualization company that serves the pharmaceutical industry.

Teaching Assistants

Zach Cutler, PhD student, Visualization / Computer Science

Logistics

Lectures: Monday and Wednesday 3:00–4:20 pm, James Fletcher Building B1
Grades: Canvas
Discussion: Slack – please join with your uid@utah.edu address and use your real name.
Lecture Slides: Drive
Technical Lectures: GitHub
Homeworks: GitHub