The world is awash with increasing amounts of data, and we must keep afloat with our relatively constant perceptual and cognitive abilities. Visualization provides one means of combating information overload, as a well-designed visual encoding can supplant cognitive calculations with simpler perceptual inferences and improve comprehension, memory, and decision making. Moreover, visual representations may help engage more diverse audiences in the process of analytic thinking.

By the end of this course, you should expect to be able to:

  1. Understand key visualization techniques and theory.
  2. Design, evaluate, and critique visualization designs.
  3. Wrangle and explore datasets through visualization using Tableau.
  4. Implement interactive data visualizations using Vega-Lite and D3.js.
  5. Develop a substantial visualization project.
  6. Read and discuss visualization research papers.

Schedule & Readings

Week 3: Exploratory Data Analysis (EDA)

Week 10

Mon 4/19

No Class (Patriot's Day)

Wed 4/21

Mapping & Cartography [ Slides (PDF) · Video Recording ]

Week 11: Critical & Feminist Visualization

Mon 4/26

Guest Lecture: Critical Mapping with Meghan Kelly (Dartmouth) [ Video Recording ]

Wed 4/28

Guest Lecture: Data Feminism with Catherine D'Ignazio (MIT DUSP) [ Video Recording ]

Week 14

Mon 5/17

Final Lecture: 6.859 Retrospective [ Video Recording ]

Wed 5/19

Final Projects Presentations

  • Due Final Project Deliverables


Diversity & Inclusion. This course welcomes all students of all backgrounds. You should expect and demand to be treated with respect by your classmates and the course staff and, reciprocally, treat your classmates and course staff with respect. Each of us is responsible for creating a safer, more inclusive environment. Unfortunately, incidents of bias or discrimination do occur, whether intentional or unintentional. They contribute to creating an unwelcoming environment for individuals and groups at the university. If any incident occurs that challenges this commitment to a supportive, diverse, inclusive, and equitable environment, please let Arvind and the teaching staff know so that the issue can be addressed.

Individual assignments. The first four assignments are solo assignments, and should be completed without collaboration. You are encouraged to ask the instructor and/or TAs for advice during office hours, and to use Slack to obtain answers to questions from other students.

Team Projects. Team projects, of course, encourage collaboration. You are encouraged to work together on all parts of the project, and must ensure that every team member is involved in all aspects of the project (design, coding, and documentation). Although the team will receive a single grade, each team member will be asked to identify their own work product to ensure equitable divison of labor. Participation in team check-in meetings and project presentations will be evaluated on an individual basis.

Reuse of third-party material. Unless otherwise stated in an assignment, you are free to use any third-party code, whether as libraries or code fragments, and to adopt any idea you find online or in a book as long as it is publicly available and appropriately cited (see the section on code in the MIT Handbook on Academic Integrity for details). Please include these citations directly on your visualization(s) as well as part of any required writeups.

Lateness & Slack Days. You have 5 slack days, which you can use as you wish for assignments 0–4. These days are to be used for minor illnesses, special occasions (such as religious holidays, interviews and sports meet events), and unexpected problems. Additional extensions will be granted only for serious medical issues with a written note from S3 (for undergraduate students), the EECS graduate student office (38-444), or GradSupport at the MIT Office of Graduate Education (OGE). Late submissions not covered by a slack day will incur a penalty of 10% of the total available grade for each day of lateness. Note also that while we will endeavor to return graded work to you as soon as possible, if you use slack days you may miss a grading cycle and receive feedback in the following week.

Class Participation

This course is mixes traditional lectures with more hands-on design exercises, interactive activities, and project presentations. Your class participation grade assesses your engagement across this spectrum of activites, and also considers your participation in posing and answering questions on Slack.

Reading Commentaries & Exercises

Most lectures have one required and several optional readings (or preparatory exercises) associated with it. Lectures will assume that you have read, and are ready to discuss, the day’s required reading. To facilitate the conversation, you are expected to submit a 1–2 paragraph commentary about each required reading on its nb page by 12pm EST on the day of the lecture. You comment should be visible to the entire class but may be optionally marked as anonymous (though we encourage you to post comments attributed to your name, as a way of building community within the class).

Commentaries should not merely summarize the reading, but rather should do one or more of the following:

  1. Critique the arguments made in the paper.
  2. Analyze of implications or future directions for work discussed in lecture or readings.
  3. Connect to concepts discussed in lecture to clarify or elaborate on some point or detail.
  4. Pose insightful questions, or answer other people’s questions.

Commentaries will be graded on a 3-point check, check-plus, check-minus scale. We will drop your two lowest commentary scores for the semester (e.g., you may choose to skip two readings without penalty).


Material for this class has been adapted from classes taught by Jeffrey Heer at the University of Washington, Maneesh Agrawala at Stanford University, Hanspeter Pfister at Harvard University, Tamara Munzner at the University of British Columbia, Jessica Hullman and Nick Diakopoulos at Northwestern University, Niklas Elmqvist at the University of Maryland, College Park, Enrico Bertini at New York University, and Sheelagh Carpendale at Simon Fraser University. Thanks also to Michael Correll at Tableau Research.

The class draws heavily on materials and examples found online, and we try our best to give credit by linking to the original source. Please contact us if you find materials where credit is missing or that you would rather have removed.