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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. Yet the digital tools for transforming data into visualizations still require low-level interaction by skilled human designers. As a result, producing effective visualizations can take hours or days and consume considerable human effort.

In this course we will study techniques and algorithms for creating effective visualizations based on principles and techniques 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 programming and data analysis assignments as well as a final programming project.

There are no official prerequisites for the class, but the class is aimed at graduate students and advanced undergraduates. However, familiarity with the material in CS147, CS 148 and CS142 can be useful. Even more important is a basic working knowledge of web-programming, especially Javascript and D3. Experience with data analysis applications (e.g. Excel, Matlab, R) is also helpful. The final project can be developed using any suitable language or application. While we will cover a little bit of Javascript/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 can help you find the relevant information as you need it.

Contact me (Maneesh) via Piazza if you are worried about whether you have the background for the course.


Week 1
M Sep 24: The Purpose of Visualization
    Submit Response | Slides
   Assigned: Assignment 1 (due Oct 1 by noon)
   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.
   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 Sep 26: Data and Image Models
    Submit Response | Slides
   Required readings
        Chapter 1: Information Visualization, In Readings in Information Visualization. Card, et al. (pdf)
        Chapter 2: Graphical Integrity, In The Visual Display of Quantitative Information. Tufte.
Week 2
M Oct 1: Visualization Design
    Submit Response | Slides
   Due (by noon): Assignment 1
   Assigned: Assignment 2 (due Oct 15 by 4:30pm)
   Required readings
        Chapter 3: Sources of Graphical Integrity. In VDQI. Tufte.
        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.
   Optional readings
        Sharing Visualizations with the World. ACM Queue. (web)
W Oct 3: Exploratory Data Analysis
    Submit Response | Slides
   Required readings
        Polaris. Stolte, Tang, and Hanrahan. IEEE TVCG, 8(1), Jan 2002. (pdf)
        Chapter 2: Macro/Micro Readings. In Enviinsioning Information. Tufte.
        Chapter 8: Data Density and Small Multiples. In VDQI. Tufte.
        Chapter 4: Small Multiples. In Envisioning Information. Tufte.
   Optional readings
        Dynamic queries, starfield displays, and the path to Spotfire. Shneiderman. (web)
        Exploratory Data Analysis. NIST Engineering Statistics Handbook. (web)
        Exploratory Data Analysis. Wikipedia. (wikipedia)
Week 3
M Oct 8: Perception
    Submit Response | Slides
   Required readings
        Perception in visualization. Healey. (html)
        Graphical perception. Cleveland and McGill. (jstor)
        Chapter 3: Layering and Separation. In Envisioning Information. Tufte.
   Optional readings
        Gestalt and composition. In Course #13, SIGGRAPH 2002. Durand. (pdf)
        The psychophysics of sensory function. Stevens. (jstor)
        Crowdsourcing Graphical Perception. Heer and Bostock. ACM CHI 2010. (pdf)
W Oct 10: Interaction
    Submit Response | Slides
   Required readings
        Postmortem of an example, Bertin. (pdf)
        Visual information seeking, Ahlberg and Shneiderman. (html)
        Visual queries for finding patterns in time series data, Hochheiser and Schneiderman. (pdf)
   Optional readings
        Generalized selection via interactive query relaxation. Heer, Agrawala & Willett. (html)
        The visual design and control of the trellis display. Becker, Cleveland and Shyu. (pdf)
        Exploration of the Brain’s White Matter Pathways with Dynamic Queries. Akers et al. (html)
        LA Homicides
        Classic systems from


  1. The Visual Display of Quantitative Information (2nd Edition). E. Tufte. Graphics Press.
  2. Envisioning Information. E. Tufte. Graphics Press.

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

Teaching Staff

Instructor: Maneesh Agrawala
    Office Hours: 11-noon Mondays, Gates 364 and by appointment
Course Assistant: Gracie Young
    Office Hours: TBD and by appointment
Course Assistant: Vera Lin
    Office Hours: TBD 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: For assignments we will deduct 10% for each day (including weekends) the 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.