Visualization 2010 Course Announcement

This is the historic announcement for the first time I offered a visualization class in 2010. It was an experiment that keeps going to this day (2020 as I write this). It’s pretty interesting that even at the beginning, I had the same ideas as to the goals of the class.

This is a course announcement for the first (2010) time I tried to teach a visualization course.

Course Announcement for Spring 2010:

CS838: Visualization: getting from data to understanding

Spring Course Announcement:

CS838: Visualization: getting from data to understanding

Instructor

Mike Gleicher

Time

Tuesday/Thursday 11-12:15

Prerequisites

none

Intended Audience

students who work with data and need to use visualizations effectively and students who are interested in creating tools to help people work with data

Credits

nominally 3, but variable credit possible (especially for dissertators)

Please contact the instructor if you are interested.

This course will explore the foundations of visualization: how we turn data into pictures to help in understanding or communicating it. We’ll cover visualization in the broad sense: including scientific visualization, information visualization (the presentation of abstract data), and visual analytics (the use of interactive tools for exploring large and/or complex data sets).

The content (the topics, not the teaching style) of this course is modeled after the visualization courses at Harvard (cs171) and Berkeley (cs294). Here, we will teach the class in a bit more of a “seminar” style - using class time more for discussion and student presentations than lectures.

Overview

Visualizations range from crayon sketches on the back of a napkin to immersive virtual reality display of the fluid dynamics around an airplane; from a bar chart in excel to a fancy, realistic 3D model.

Our goals are to understand the principles that lead to effective visualizations across this range (design, the use of color and motion, basic design patterns, dealing with high-dimensional data, …),specific visualization designs and problems (treemaps, scatterplot matrices, focus+context, volume visualization, …),as well as looking at the kinds of systems and tools that support the creation of good visualizations.

By the end of the course, we will learn how to design effective visualizations for the kinds of data we want to interpret and understand the kinds of tools that support the creation of such visualizations.

For a more complete description, I steal this from the course at Berkeley (with 2 sentences removed):