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Visual media are increasingly generated, manipulated, and transmitted by computers. When well designed, such displays capitalize on human facilities for processing visual information and thereby improve comprehension, memory, inference, and decision making. Moreover, visual representations may help to engage diverse audiences in the process of analytic, data-driven thinking.

In this course we will study techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology and cognitive science. The course is targeted both towards students interested in using visualization in their own work, as well as students interested in building better visualization tools and systems. In addition to participating in class discussions, students will have to complete several short data analysis and visualization design assignments as well as a final project.

There are no official prerequisites for the class, but the class is aimed at graduate students and advanced undergraduates. Familiarity with the material in CS147, CS148 and CS142 can be useful. Most important is a basic working knowledge of, or willingness to learn, web-programming, especially JavaScript, Vega-Lite and D3. Experience with data analysis applications (e.g. Rm Python, Excel) is also helpful. While we will cover a little bit of JavaScript/Vega-Lite/D3 in class, most of the other APIs, applications and languages will not be taught in the course. However many introductory tutorials at the level required for the class are available on the web and we will help you find the relevant information as you need it.

*Contact us via Piazza if you are worried about whether you have the background for the course.

Learning Goals

The goals of this course are to provide students with the foundations necessary for understanding and extending the current state of the art in visualization. By the end of the course, students will have:

  • 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 exploratory data analysis and network analysis.
  • Practical experience building and evaluating visualization systems.
  • The ability to read and discuss research papers from the visualization literature.


  1. The Visual Display of Quantitative Information (2nd Edition). E. Tufte. Graphics Press.
  2. Envisioning Information. E. Tufte. Graphics Press.
  3. Optional. Interactive Data Visualization for the Web (2nd Edition). Scott Murray. O’Reilly Press. [Read Online] [Code Examples on Github]

Your best bet is to order them online. Please order soon. Readings will be assigned in the first week of class.


Week 1
M Jan 6: The Purpose of Visualization
    Submit Response | Slides
   Assigned: Assignment 1 (due Jan 13 by noon)
   Required Notebooks
        Introduction to Vega-Lite/Altair. (Javascript/Observable) (Python/Colab)
   Required readings
        Chapter 1: Information Visualization, In Readings in Information Visualization. Card, et al. (pdf)
   Optional readings
        Decision to launch the Challenger, In Visual Explanations. Tufte. (pdf)
        Representation and Misrepresentation. (Critique of Tufte's analysis). Boisjoly et al. (web)
        Graphs in Statistical Analysis. F. J. Anscombe. The American Statistician. (jstor)
W Jan 8: Data and Image Models
    Submit Response | Slides
   Required Notebooks
        Data Types, Graphical Marks, and Visual Encodings. (Javascript/Observable) (Python/Colab)
   Required readings
        Chapter 1: Graphical Excellence, In The Visual Display of Quantitative Information. Tufte.
        Chapter 2: Graphical Integrity, In The Visual Display of Quantitative Information. Tufte.
        Chapter 3: Sources of Graphical Integrity. In VDQI. Tufte.
   Optional readings
        Level of Measurement. (Wikipedia)
        On the theory of scales of measurement. Stevens. (jstor)
Week 2
M Jan 13: Visualization Design and Redesign
    Submit Response | Slides
   Due (by noon): Assignment 1
   Assigned: Assignment 2 (due Jan 27 by 4:30pm)
   Required Notebooks
        Data Transformation. (Javascript/Observable) (Python/Colab)
   Required readings
        Design and Redesign in Data Visualization. Viegas and Wattenberg. (web)
        The Power of Representation. Chapter 3 In Things that Make Us Smart. Norman. (pdf)
   Optional readings
        Chapter 4: Data-Ink and Graphical Redesign. In VDQI. Tufte.
        Chapter 5: Chartjunk. In VDQI. Tufte.
        Chapter 6: Data Ink Maximization and Graphical Design. In VDQI. Tufte.
        The representation of numbers. Zhang and Norman. (pdf)
W Jan 15: Exploratory Data Analysis
    Submit Response | Slides
   Required Notebooks
        Scales, Axes and Legends. (Javascript/Observable) (Python/Colab)
   Required readings
        Polaris. Stolte, Tang, and Hanrahan. IEEE TVCG, 8(1), Jan 2002. (pdf)
   Optional readings
        Voyager. Wonsuphawasat et al. IEEE TVCG, 22(1), 2016. (pdf)
Week 3
M Jan 20: MLK Day - No Class
W Jan 22: Using Space Effectively
    Submit Response | Slides
   Required Notebooks
        Multi-View Composition. (Javascript/Observable) (Python/Colab)
   Required readings
        Graphical Methods for Data Presentation. Cleveland. (jstor)
        Chapter 8: Data Density and Small Multiples. In VDQI. Tufte.
        Chapter 2: Macro/Micro Readings. In Envisioning Information. Tufte.
        Chapter 4: Small Multiples. In Envisioning Information. Tufte.

Teaching Staff

Instructor: Maneesh Agrawala
    Office Hours: 1:30-2:30p Thursdays, Gates 364 and by appointment.
Course Assistant: Juliette Love
    Office Hours: 7:00-8:00pm Tuesdays, Lathrop Tech. Lounge and by appointment.

To contact us please use Piazza. This is the fastest way to get a response.

Assignments and Requirements

Class Participation (10%)
Assignment 1: Visualization Design (10%)
Assignment 2: Exploratory Data Analysis (15%)
Assignment 3: Creating Interactive Visualization Software (25%)
Final Project (40%)

Late Policy: We will deduct 10% for each day an assignment is late.

Plagiarism Policy: Assignments should consist primarily of your 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.