Data Visualization & Information Aesthetics

PGDV 5100 › Parsons › Fall 2017

This is a seminal course on information design and aesthetics. Students will study graphical theory, graph grammar, and investigate hierarchies, patterns, and relationships in data structures. Students will examine the role of scale, proportion, color, form, structure, motion, and composition in data visualization. Using computational methods, students will create drawings, graphs, indexes, and maps that explore the database as cultural form. The function of this course is to build a community among the students and orient them to the whole program.


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 cloud-based visualization platforms, digital design software, or paper prototyping techniques, students will create drawings, graphs, indexes, and maps. 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 have process documents available to share during each class session. Active contribution during class 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 3
  • Exercise 2: Visualize quantities, categories and summarized data – due: week 5
  • Exercise 3: Visualize textual and qualitative data – due: week 7
  • Exercise 4: Visualize geospatial data – due: week 9
  • Present a research report on subject assigned during the first class session – due on individually assigned date
  • Document research and design process in the Learning Portfolio – 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

Week Date In class Due Assigned
1 30 Aug. Introduction, Syllabus review, Overview of Data Visualization, Catalog & Classify
2 6 Sept. Mapping Time Research ReportsReading Exercise 1
3 13 Sept. Review Exercise 1Exercise 1
No meeting 20 Sept.
4 27 Sept. Mapping Quantities, Categories, and Summarized DataResearch ReportsExercise 2
5 4 Oct. Review Exercise 2Exercise 2
6 11 Oct. Mapping Textual and Qualitative DataResearch Reports Exercise 3
7 18 Oct. Review Exercise 3Exercise 3
8 25 Oct. Mapping SpaceResearch Reports Exercise 4
9 1 Nov. Review Exercise 4Exercise 4 Final Project
10 8 Nov. Mapping Hierarchies and Networks. 
Review Final Project SourcesFinal Project Sources, Research Reports Final Project Proposal
11 15 Nov. Review Final Project ProposalFinal Project Proposal Final Project Prototype
No meeting 22 Nov.
12 29 Nov. Lab, Group DiscussionResearch Reports
13 6 Dec. Review Final Project PrototypeFinal Project Prototype
14 13 Dec. Lab, Individual DiscussionResearch Reports
15 Mon 18 Dec. Review Final ProjectFinal Project

The week-by-week agenda for each class meeting will be updated incrementally on the Schedule page.

Final Grade Calculation

10%: Exercise 1 – Mapping Time
10%: Exercise 2 – Mapping Quantities, Categories, and Summarized Data
10%: Exercise 3 – Mapping Textual and Qualitative Data
10%: Exercise 4 – Mapping Space
10%: Research Report
10%: Learning Portfolio
10%: Class Participation + Attendance
30%: Final Project

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