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Data, Design and Social Justice


  • Jennifer Wilson


This class will explore how data visualization and analysis can be used to address issues of social justice. Our world is increasingly filled with data, and developing skills to make sense of it and communicate the results visually provide students with powerful tools to make their arguments more effective. We will employ a variety of techniques and perspectives drawing from the fields of statistics, cognitive science, perceptual psychology, coding, and information design. Looking at a series of case studies, we will discuss how to interpret quantitative analyses we encounter in the world as well as how to employ these skills for positive change in our world. This course counts towards the Subject Requirement for the Journalism + Design Major. This course also counts as an elective for the Media Studies track of the Culture and Media major.

Learning Outcomes

  • be familiar with a variety of different information visualization methods and understand their strengths and weaknesses
  • understand basic descriptive statistics
  • have familiarity with basic design principles as applied to information visualization
  • gain skills in creating your own visualizations
  • have deeper understanding of existing graphical and quantitative arguments
  • develop an understanding of how data can be used to support and argument
  • feel comfortable with a number of different software packages and data repositories
  • develop collaborative problem-solving and critique skills
  • incorporate text, data and images to create and present a position

Topic Outlines

  • Data visualization
  • Visual storytelling
  • Visual perception
  • Ethics
  • Social Justice


  • Information Visualziation, Colin Ware
  • Do Statistics lie?, Unknown author
  • The Power of Representation, Norman
  • Maps which shouldn't be maps, Matthew Ericson,
  • Head-first statistics, Dawn Griffiths
  • The seven deadly sins of statistical misinterpretation, and how to avoid them, Winnifred Louis and Cassandra Chapman;
  • Discussing Design, Adam Connor & Aaron Irizarry
  • A tour through the Visualization Zoo, Jeffrey Heer, Michael Bostock and Vadim Ogievetsky;

Grading Rubric

Student participation in class is crucial for the success of a seminar: not only do proper preparation and participation help with individual learning, but they contribute to everyone’s learning through the collaborative process. In this course, students will be expected to complete all readings and response questions before each class. During class, students will be expected to contribute to discussions and work on all assigned activities. There will be biweekly assignments: these will be varied, reflecting the interdisciplinary nature of the subject. Students are encouraged to discuss and work with each other in completing all homework assignments, although the finished product must reflect their own synthesis of the material. Evaluation of the final project will be broken into several stages including a preliminary proposal, first draft, final presentation and completed project.


  • Class Participation/Response Papers - 15%
  • Biweekly Assignments - 40%
  • Final Project Proposal (first draft and final proposal) 5% First Draft 10% Presentation 10% Final Paper/Poster 20%


The class, as well as case studies for how information visualization is used to construct an argument. There will also be many online resources to assist with technical and software questions. Class periods will be divided between discussion of the readings and data vis examples, short lectures on new mathematical and design concepts, and collaborative problem-solving and design sessions.

Course Resources

Data Design Syllabus F17.doc.docx

Syllabus for the course
Uploaded by Common Syllabi on 2023-07-03