CS 4460 B - Intro. to Information Visualization

Instructor: John Stasko
Fall 2014
Tue,Thu 3:00 - 4:30 pm
Van Leer, room C241

Information visualization centers around helping people explore or explain data through interactive software that exploits the capabilities of the human perceptual system. A key challenge in information visualization is designing a cognitively useful spatial mapping of a dataset that is not inherently spatial and accompanying the mapping by interaction techniques that allow people to intuitively explore the dataset. Information visualization draws on the intellectual history of several traditions, including computer graphics, human-computer interaction, cognitive psychology, semiotics, graphic design, statistical graphics, cartography, and art. The synthesis of relevant ideas from these fields with new methodologies and techniques made possible by interactive computation are critical for helping people keep pace with the torrents of data confronting them.

Information visualization methods are applied to data from many different application domains, including:

  • Political reporting and forecasting - as seen on TV and in the papers in election season.
  • News reporting - see the interactive visualizations used by the New York Times, Wall Street Journal, Slate, etc.
  • Social science and economics data, such as census and other surveys, and micro and macro economic trends.
  • Social networking and web traffic, to understand patterns of communication
  • Business intelligence and business dashboards - to forecast sales trends, understand competitive marketplace positions, allocate resources, manage production and logistics.
  • Text analysis - to determine trends and relationships for literary analysis and for information retrieval.
  • Criminal investigations - to portray the relationships between event, people, places and things.
  • Performance analysis of computer networks and systems.
  • Software engineering - developing, debugging and maintaining software.
  • Bioinformatics, to understand DNA, gene expressions, systems biology.
  • and many others.

The objectives of the course are

  • Learn to apply an understanding of human perceptual and cognitive capabilities to the design of information visualizations
  • Understand the wide variety of existing visualization techniques and know when to apply each for different types of data and goals
  • Know how information visualizations use dynamic interaction methods to help people understand data
  • Develop skills in critiquing different visualization techniques in the context of user goals and objectives
  • Learn how to use and critique existing systems for creating visualizations
  • Learn the principles involved in designing and implementing effective information visualizations

The course will follow a lecture/seminar style with much discussion of assigned readings, as well as viewing of videos and hands-on experience with research and commercial information visualization tools.

With respect to textbooks, we will be using the book: Now You See It by Stephen Few, Analytics Press 2009, in the course. This book has helpful design guidance that will be useful even after this course. In addition, we will be using some chapters from a draft of Tamara Munzner's new textbook Visualization Analysis and Design that is available online as a pdf draft. Also highly recommended, especially for visualization junkies, is the classic Envisioning Information by Edward Tufte, Graphics Press 1990.

Grading will be based on class participation, homework assignments involving application of class principles, a midterm and final exam, and a semester gourp-based design and implementation project. The weight of each assignment can be found on the assignments page.

Academic integrity: Unless explicitly stated otherwise, you are expected to do your assignments and work on your own. Your project will be a collaborative effort among a group of students. For it, you may use libraries and code fragments from sources on the web that you integrate into an overall working system. Your source code should indicate what code is imported and used as is, what code is imported and modified, and what code is original. It is appropriate to discuss your project with others to gain ideas and feedback and help with sticky problems. It is not appropriate to find an existing infovis system, modify it and submit it as your own work. If in doubt, confer with your instructor.