Course Announcement

by Mike Gleicher on November 11, 2016

CS765 Data Visualization

Note: this is a new course number. Prior versions of this class were taught as special topics classes. The 2015 Offering of the class was the most recent one.

Unofficial Title: Visualization: getting from data to understanding

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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). Visualization is a mix of perceptual psychology, cognitive science, design, computer graphics, data analysis, statistics, human computer interaction, system building, etc. The course is aimed to serve two different types of students:

  • students who work with data and want to understand how to better use visualizations in their work (e.g. students in the sciences or humanities)
  • students who are interested in creating tools to help people work with data (e.g. computer scientists and statisticians)

This class is more about what pictures to make to understand data than how to make them. We will spend a lot of time understanding design principles. We will not spend lots of time talking about how to program visualizations, or how to use tools to make visualizations.

This course was taught in the past as a special topics class. The most recent offering (2015) will give you a good sense of what the class is like: the 2017 offering will be similar. We’ll cover similar topics, and use a similar class structure.

Some Basic Data:

Instructor: Mike Gleicher

Time:  1-2:15, Monday, Wednesday, Friday. Note that the class is “over-scheduled” we will meet, on average, twice a week. The class will meet in 212 Educational Sciences.

Intended Audience: Graduate students in CS or in some domain where data is used. Advanced undergraduates are welcomed, although on a space available basis.

Prerequisites: None. If you have programming experience, you will have the opportunity to make use of it. If you don’t know how to program, there will be other ways to do the assignments. Some basic statistics is useful too.

Course format: class lectures, discussions (in class and online), readings, design exercises. There will be small “projects.”

Readings: mostly from an online reader. The textbook will be available online through the library. Check the 2015 class readings for an idea of what it might be like, although the list will be updated a bit.


Visualizations range from crayon sketches on the back of a napkin to immersive virtual reality displays 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. You can get an idea of the kind of topics by looking at the syllabus from 2015.

What is this class?

If you are interested in working with data (or helping others work with data), especially in understanding the “human side” of it, then this class is for you. This class is more about what pictures to make to understand data than how to make it. We will spend a lot of time understanding design principles. We will not spend lots of time talking about how to program them. If you can’t program, don’t worry. We’ll find other ways for you to make pictures. We will not teach you to program. If you can program (and like to do it), don’t worry. We’ll let you do some programming to make pictures. If you only care about getting help with your pet data set from your research, this class may or may not help you. You’ll learn lots of general visualization concepts that will help you with your specific problems in the future, and you might have opportunities to try things on your own data sets for the assignments and projects. Plus, you’ll meet a whole bunch of people who will know a lot about visualization by the end of the semester – so you can get them to help you.


The list of topics is still being developed. Here is a list of what we covered in the previous editions of the class. For this year, we’ll probably spend a little less time on the “what and why” in the beginning, to make time for a little more “how” later on.

week 2015 Vis Class (CS838/638) 2012 Vis Class (CS838/638)
1 What is Vis What is Vis
2 What kinds of Vis and Why Why Vis
3 Abstraction Evaluation
4 Evaluation Perception
5 Perception Encoding
6 Color Color
7 Encodings and Layouts Multi-Variate
8 Graphs and Networks Dealing with Scale
9 Interaction and Multiple Views Interaction
10 Dealing with Scale Case Studies
11 More Dealing with Scale Graphs and Networks
12 Comprehensibility and Uncertainty Animation and Presentation
13 3D and D3 Visual Design
14 Scientific Data Sets 3D and SciVis

Class Activities

This class will involve substantial amounts of reading and online discussion. There will be weekly readings with online discussions and weekly “show and tell” assignments (where students find and critique visualizations). We will have periodic in-class “design exercises” (generally paper and pencil). There will be a small number of “mini-projects” where students will design and create visualizations.

Even more information?

Coming soon – this is just a preliminary announcement.


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