Data Visualization & Information Aesthetics

Fall 2019 › Parsons › PGDV 5100

This is a foundational course on information design and aesthetics. Students will study the fundamentals of design and the grammar of graphics while investigating hierarchies, patterns, and relationships in data structures. Students will examine the role of scale, proportion, color, form, structure, motion, and composition in data visualization. The intent of this course is to build a community among the students and orient them to the program and the larger discipline.


This course is an introduction to data visualization, promoting data literacy and visualization competencies for visual artists, designers, and analysts. With a focus on social engagement, this course prepares students with the critical skills to advocate visually and the intellectual context to engage a world in which data increasingly shapes opinion, policy, and decision making.

Students will learn to curate and uncover insights from large and complex data sets. Using code-based visualization platforms, digital design software, or paper prototyping techniques, students will create plots, graphs, indexes, and maps that explore the database as cultural form. Students will familiarize themselves with the necessary vocabulary to communicate and collaborate with data visualization professionals in future contexts. Throughout the course, students will work with Canvas, this blog, and GitHub to collect and share resources and submit assignments. A series of presentations, screenings, readings, and discussions exposes students to artists and designers working in the context of data visualization and the digital arts. Each student will select a research topic, and present a research report in conjunction with an in-class discussion.

Assignments are invitations to invent and experiment. Creative and ambitious experiments will receive high marks, while obvious and easily attained solutions will not – competence alone is not our goal here. The complexity of the assignments increases as the semester progresses. Students are required to document their iterative design process and expected to have process documents available to share during each class session. Active contribution during class discussions and critiques is required. All assignments must be completed to pass the course. Assignments are only considered complete when checked into GitHub. Late assignments and attendance will reduce grades proportionally.

Learning Outcomes

  1. Develop a deep understanding of the various methods and techniques of modern data visualization as well as its historical context.
  2. Develop skills to design effective visual communication and information displays by learning a framework for educated exploration and invention.
  3. Gain experience in describing, analyzing, and evaluating various data visualization approaches through presentations and critiques.

Assessable Tasks

  • Exercise 1: Visualize time – due: week 4
  • Exercise 2: Visualize quantitative & categorical data – due: week 7
  • Exercise 3: Visualize textual & qualitative data – due: week 11
  • Present a research report on subject assigned during the first class session – due on individually assigned date
  • Document research and design process in the GitHub repo – due weekly
  • Collect sources for the final project – due: week 10
  • Proposal for the final project – due: week 11
  • Create a prototype for the final project – due: week 13
  • Create and Demonstrate the final project – due: week 15

Course Outline

The week-by-week agenda for each class meeting will be updated incrementally on the Schedule page, but the overall plan is as follows:

Week Date In class Due Assigned
1 28 Aug. Syllabus review, Overview of Data Visualization, Review Semiology of Graphics Complete catalog entries
2 4 Sept. Catalog & Classify discussion & P5.js Lab Exercise 1: Mapping Time
3 11 Sept. Presentations & discussion
4 18 Sept. Exercise 1: Critique Exercise 1
5 25 Sept. Presentations & discussion Exercise 2: Mapping Quantities, Categories, and Summarized Data
6 2 Oct. Exercise 2: review first drafts
No meeting 9 Oct.
7 16 Oct. Exercise 2: final critique Exercise 2
8 23 Oct. Presentations & discussion Exercise 3: Mapping Textual & Qualitative Data
9 30 Oct. Exercise 3: review first drafts Final Project
10 6 Nov. Presentations & discussion, final project ideas Project ideas, data sources, and sketches Final Project Proposal
11 13 Nov. Exercise 3: final critique Exercise 3, Final Project Proposal Final Project Prototype
12 20 Nov. Presentations & discussion, project meetings
No meeting 27 Nov.
13 4 Dec. Presentations & discussion, review prototypes Final Project Prototype
14 11 Dec. Lab, Individual Discussion
15 18 Dec. Review Final Project Final Project

Final Grade Calculation

10%: Exercise 1 – Mapping Time
15%: Exercise 2 – Mapping Quantities, Categories, and Summarized Data
25%: Exercise 3 – Mapping Spatial Data
15%: Research Presentation
10%: Process Documentation
25%: Class Participation & Attendance

For details on how work will be evaluated (as well as general expectations for your participation in this course), see the Grading & Policies page.