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Well designed visualizations capitalize on human facilities for processing visual information and thereby improve comprehension, memory, inference, and decision making. 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.

There are no official prerequisites for the class, but familiarity with the material in CS147, CS148 and CS142 is especially useful. Most important is a basic working knowledge of, or willingness to learn, web-programming, especially JavaScript, Vega-Lite and D3.js. While we will cover a little bit of Vega-Lite and D3.js in class, we will also expect students learn some introductory material, especially about Javscript on their own, as necessary. Tutorials on Javascript are available on the web and we will help you find the relevant information as you need it.

*Contact us via Slack 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 using Vega-Lite and D3.js.
  • 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 Textbook. Visualization Analysis and Design. Tamara Munzner. A K Peters Visualization Series. CRC Press.

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


Week 1
W Sep 27: The Purpose of Visualization
    Submit Response | Slides
   Assigned: Assignment 1 (due Oct 2 by 11:30am)
   Required notebooks
        Introduction to Vega-Lite (Javascript/Observable)
   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)
Week 2
M Oct 2: Data and Image Models
    Submit Response | Slides
   Due (by 11:30am): Assignment 1
   Assigned: Assignment 2 (due Oct 16 by 11:30am)
   Required notebooks
        Data Types, Graphical Marks, and Visual Encodings (Javascript/Observable)
   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)
M Oct 2 4:30-5:30pm: Optional Zoom Session - Intro to Observable and Data Wrangling
   Optional readings
        Introduction to Observable and Data Wrangling (Javascript/Observable)
W Oct 4: Visualization Design and Redesign
    Submit Response | Slides
   Required notebooks
        Data Transformation (Javascript/Observable)
   Required readings
        Chapter 4: Data-Ink and Graphical Redesign. In VDQI. Tufte.
        Design and Redesign in Data Visualization. Viegas and Wattenberg. (web)
   Optional readings
        The Power of Representation. Chapter 3 In Things that Make Us Smart. Norman. (pdf)
        The representation of numbers. Zhang and Norman. (pdf)
        Chapter 5: Chartjunk. In VDQI. Tufte.
        Chapter 6: Data Ink Maximization and Graphical Design. In VDQI. Tufte.
        Vega-Lite Excercise (will use in class) (JavaScript/Observable)
F Oct 6 2-3pm: Optional Zoom Session - Tableau Tutorial
   Optional readings
        Tableau Tutorial (without solution) (pdf)
        Tableau Tutorial Solution (pdf)
Week 3
M Oct 9: Exploratory Data Analysis
    Submit Response | Slides
   Required notebooks
        Scales, Axes and Legends (Javascript/Observable)
   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)
W Oct 11: Chart Design
    Submit Response | Slides
   Required notebooks
        Multi-View Composition (Javascript/Observable)
   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.
Week 4
M Oct 16: Interaction
    Submit Response | Slides
   Due (by 11:30am): Assignment 2
   Assigned: Assignment 3 (due Oct 30 by 11:30am)
   Required notebooks
        Interaction (Javascript/Observable)
   Required readings
        Interactive dynamics for Visual Analysis. Heer and Shneiderman. (pdf)
   Optional readings
        The death of interactive infographics? Baur. 2017 (web)
        In Defense of Interactive Graphics. Aisch. 2017 (web)
        Dynamic queries, starfield displays, and the path to Spotfire. Shneiderman. (web)
        Visual queries for finding patterns in time series data, Hochheiser and Schneiderman. (pdf)
        Postmortem of an example, Bertin. (pdf)
W Oct 18: Introduction to D3
    Submit Response | Slides
   Required notebooks
        Introduction to D3 (Javascript/Observable)
   Optional readings
        Let's Make a Scatterplot (will use in class) (JavaScript/Observable)
        Observable D3 gallery (html)
        Mike Bostock's notebooks for learning D3 (html)
        D3: Data Driven Documents. Bostock et al. (pdf)
Week 5
M Oct 23: D3 Tutorial
    Submit Response | Slides
   Required notebooks
        Making D3 Charts Interactive (Javascript/Observable)
        Let's Make a Scatterplot (will use in class) (Javascript/Observable)
        D3 Exercises (will use in class) (Javascript/Observable)
W Oct 25: 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)
Week 6
M Oct 30: Visual Explainers
    Submit Response | Slides
   Due (by 11:30am): Assignment 3
   Assigned: Final Project: Proposal (due Nov 6 by 11:30am)
   Required readings
        The Pudding (read through at least 3 articles in detail) (html)
        Design for an Audience, Corum. (html)
        Narrative Visualization: Telling Stories with Data, Segel and Heer. (pdf)
   Optional readings
        Communicating with Interactive Articles. Hohman, Conlen, Heer and Chau. (html)
        Ice World: Visualizing Science at the New York Times, Corum. (html)
W Nov 1: Color
    Submit Response | Slides
   Required readings
        Color and information, In Envisioning Information, Tufte.
        Color Naming Models for Color Selection, Image Editing and Palette Design. Heer and Stone. (html)
        How to pick more beautiful colors for your data visualizations. Muth. (html)
   Optional readings
        The crayola-fication of the world. Bhatia. (html)
        Colorgorical: Creating discriminable and preferable color palettes for information visualization. Gramazio, Laidlaw and Schloss. (pdf)
Week 7
M Nov 6: Animation
    Submit Response | Slides
   Due (by 11:30am): Final Project: Proposal
   Assigned: Final Project: Website, Code and Video (due Dec 10 by 8:00pm)
   Required readings
        Animated Transitions in Statistical Data Graphics. Heer and Robertson. (pdf)
        Animation: Can it facilitate? Tversky, Morrison and Betrancourt. (pdf)
   Optional readings
        Principles of Traditional Animation Applied to Computer Animation. Lasseter. (acm)
        Intuitive Physics. McCloskey. (psycnet)
        Representing motion in a static image. Cutting. (pdf)
W Nov 8: Network Layout
    Submit Response | Slides
   Required readings
        Graph Visualization: A Survey. Herman, Melancon and Marshall. (pdf)
        Hierarchical Edge Bundles. Holten. (ieee)
   Optional readings
        Let’s draw a graph. Khoury. (html)
        Visual Exploration of Multivariate Graphs. Wattenberg. (pdf)
        Improving Walker’s Algorithm to Run in Linear Time. Buchheim, Jünger and Leipert. (pdf)
        Visual Complexity - graph visualization gallery
Week 8
M Nov 13: Network Analysis
    Submit Response | Slides
   Required readings
        Centrality and Prestige of Social Network Analysis. (pp. 169-198) Wasserman and Faust. (pdf)
        Balancing Systematic and Flexible Exploration of Social Networks. Perrer and Shneiderman. (ieee)
   Optional readings
        The Structure and Function of Complex Networks (Sections 1 and 2 only pp. 1-8). Newman. (pdf)
        The Social Organization of Conspiracy. Baker and Faulkner. (pdf)
W Nov 15: Deconstructing Visualizations
    Submit Response | Slides
   Required readings
        Revision: Automated Classification, Analysis and Redesign of Chart Images.Savva et al. (html)
        Graphical Overlays: Using Layered Elements to Aid Chart Reading. Kong and Agrawala. (html)
   Optional readings
        Deconstructing and Restyling D3 Visualizations. Harper and Agrawala (html)
        Extracting References Between Text and Charts via Crowdsourcing. Kong et al. (html)
Week Thanksgiving
M Nov 20: No class due to Thanksgiving Holiday
W Nov 22: No class due to Thanksgiving Holiday
Week 9
M Nov 27: Final Project Design Review and Feedback
W Nov 29: Final Project Design Review and Feedback
Week 10
M Dec 4: Visualization and AI (Guest Lecture: Hari Subramonyam)
    Submit Response | Slides
   Required readings
        Visual analytics in deep learning: An interrogative survey for the next frontiers. Hohman et al. (IEEE)
        Interactive correction of mislabeled training data. Xiang, S. et al. (pdf)
   Optional readings
        What you see is what you can change. Sacha, D. et al. (html)
W Dec 6: Visualization and Natural Language Processing
    Submit Response | Slides
   Required readings
        Answering Questions about Charts and Generating Visual Explanations. Kim et al. (html)
        Towards Understanding How Readers Integrate Charts and Captions. Kim et al. (html)
   Optional readings
        Eviza: A Natural Language Interface for Visual Analysis, Setlur et al. (html)
        Accessible Visualization via Natural Language Descriptions. Lundgard et al. (pdf)
Su Dec 10: Final Submission
   Due (by 8:00pm): Final Project: Website, Code and Video

Teaching Staff

Instructor: Maneesh Agrawala
    Office Hours: 11am-noon Thursdays, Coupa Cafe at Y2E2 and on Zoom (arranged on Slack).
Course Assistant Jasmine Shih
    Office Hours: 3:30-4:30pm Fridays, Huang Basement.
Course Assistant Yifan Shen
    Office Hours: 10-11am Wednesdays, Huang Basement
Course Assistant Anthony Xie
    Office Hours: 4:30-5:30pm Mondays, Bytes Cafe.

We are also happy to meet by appointment. To contact us for any reason, please use Slack. 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. You may use AI-based tools, but you must note in the description that AI was used and you are solely responsible for the correctness of all of your work.