Innovations in Visualization

Information Visualization

CPSC 683, Winter 2015

The goal of Information Visualization (Infovis) is to make use of efficient visual representations that allow users to understand data, and to provide users with interaction capabilities that are designed to efficiently analyze these representations. The field of Infovis leverages computers to create meaningful interactive visualizations in order to navigate in and analyze---potentially large quantity of---abstract data; in order to gain insights; and ultimately make decisions based on these insights, for many types of datasets, tasks, and analysis scenarios.

Lecture: Tuesdays and Thursdays, 12:30 - 1:45, MS 680A.

Office Hours: By arrangement (680D).

Prerequisites: Consent of the department.


Course Overview:

The goal of this course is to introduce students to the research field of Interactive Information Visualization. The course presents both seminal and recent work in Infovis by looking at the variety of the research field. It involves:

  • Research before the field of Infovis was created.
  • Principles from perception, cognition, and design.
  • Representation of data (or data mapping to visual symbols/structures).
  • The type of data to be analyzed (e.g., tabular data, hierarchical data, graphs and networks).
  • Applications (e.g., web, text, biology, social data).
  • Interaction (e.g., navigation, transformations, details on demand)
  • Communication, storytelling, visualization literacy.
  • Evaluation methodologies and issues.

Students will be pro-active in the course, each class involving research paper presentations from students, and reading and discussing research papers. Although the class will consist mainly of lessons, these are interactive and more a discussion than a formal lesson. Also, students will do a major research project consisting of designing, implementing, and presenting their own visualization on a dataset of their choice. Students will be expected to write up the results of their project in the form of a research paper submission.


There is no particular prerequisite for this course. Having some knowledge in Information Visualization or Human-Computer Interaction is a plus. Having some programming skills is also a plus. Although there is no specific programming language to know, knowledge in Javascript and/or Java/Processing will be helpful.


  • 65% project
    • 5% Dataset, problem, preliminary design, and tasks presentation
    • 5% Design and sketch prototyping
    • 10% Initial proposal
    • 10% Update
    • 15% Implementation
    • 35% Final conference paper format report
    • 20% Oral presentation and demo
  • 15% paper presentation
    • 60% analysis, understanding, personal assessment
    • 40% communication, presentation quality
  • 20% class participation and class assignments
    • 50% Questions during lessons and paper presentations
    • 50% Class assignments

The course will not have a final written examination.


The project replaces a standard final examination, and is individual. Consider it seriously as it represents 65% of your final grade. It will basically consist of selecting one or several dataset and acquiring the data, sketching and designing a visualization addressing identified tasks, surveying existing similar visualization methods, implementing the interactive visualization, writing a project report in the format of a conference paper, and presenting the project during the last class.

You may use existing components as the base for your system, as well as any programming language or toolkit of your choice.


  • [TBA] Dataset proposal: short presentation
  • [TBA] Preliminary design (sketch, prototype, …): short presentation
  • [TBA] Initial proposal: written format
  • [TBA] Mid-term project presentation (update): short presentation
  • [TBA] Final project presentation: longer presentation
  • [TBA] Written report (conference paper format)

More details about the projects are coming.

Recommended readings:

There is no required reading for this class. However, many books are interested and here is a selection:

Information Visualization
Readings in Information Visualization, Stuart Card, Jock Mackinlay, and Ben Shneiderman.

Semiology of graphic: Diagrams, Networks, Maps
Jacques Bertin.

The Visual Display of Quantitative Information
Edward Tufte.

Envisioning Information
Edward Tufte.

Things That Make Us Smart
Don norman.

Visualization Analysis and Design
Tamara Munzner

Now You See It: Simple Visualization Techniques for Quantitative Analysis
Stephen Few.

Information Visualization: Design for Interaction (2nd Edition)
Robert Spence, Prentice Hall.

Visualizing Data
Ben Fry, O'Reilly.

Information Visualization: Perception for Design
Colin Ware, Morgan Kaufmann.

Readings in Information Visualization: Using Vision to Think
Stuart K. Card, Jock Mackinlay, Ben Shneiderman (editors), Morgan Kaufmann.

Visual Thinking for Design
Colin Ware, Morgan Kaufmann.

Online resources:

Data sources:

Interesting websites to find collections of visualizations and infographics:

Interesting Infovis related blogs:

Resources for coding visualizations:

Some tools to visualize your data: