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Getting started

CMSC 734 Information Visualization - Fall 2012

 Dept of Computer Science, University of Maryland
 Prof. Ben Shneiderman
 Teaching Assistant: Awalin Sopan

Consult the User's Guide for information on using the wiki software.

Class hours: Friday 9:00am - 11:30am Room: CSIC 3118

Ben Shneiderman Office Hours: Wednes 1-3pm Room: AVW 3177 Phone: 301-405-2680 Email:

TA: Awalin Sopan Office Hours: Tues 2-4pm. Room: AVW 4166 Email: Please email to schedule a time.

Class Email List

Syllabus with course schedule


Spotfire Accounts

Project Ideas

Please post project proposals on the wiki, with descriptive title, team member names, outside client/sponsors, and a project description, plus relevant references (papers, websites, etc.).



Books: Hansen, M., Shneiderman, B, and Smith, M. A., Analyzing Social Media Networks with NodeXL: Insights from a Connected World, Morgan Kaufmann Publishers (2011). ISBN: 9780123822291. Available from Campus Bookstore, or [1] or on Amazon: [2]

Keim, D., Kohlhammer, J., Ellis, G., and Mansmann, G. (Editors), Mastering the Information Age: Solving Problems with Visual Analytics, Eurographics Association, Goslar, Germany (2010). Available as free download (25MB, 175 pages).

Topics: What is information visualization? How is it related to scientific visualization? How does it combine with data mining? Information visualization is emerging as an important fusion of graphics, scientific visualization, database, and human-computer interaction. Dozens of innovative visualizations for 1-, 2-, 3-, and multi-dimensional data have been proposed, along with creative designs for temporal, hierarchical, and network data. This course will examine the design alternatives (overviews, dynamic queries, zooming, etc.), algorithms and data structures, coordinated views, plus human factors evaluations of efficacy for a variety of tasks and users.

Students will read current literature and conduct collaborative projects to design, implement, apply, and/or evaluate existing or novel visualizations. Mid-term and final exams will be given, so the course qualifies for MS and PhD comps.

Homework: Students will (1) read and critique papers, (2) critique a web-based visualization (3) use existing information visualization tools, such as Spotfire, Tableau, TimeSearcher, HCE, Treemap, LifeLines2, and NodeXL to build useful visualizations on data sets (pairs of students), (4) conduct a major team project to create, extend, or apply an information visualization tool to deal with a realistic problem, and conduct case study evaluations (teams of 3-5 students).

Grading: Term project (35%), Exams (17% & 21%), Homeworks & Participation (27%)

Readings: Key papers will be assigned for reading, including chapters of "Illuminating the Path: The Research and Development Agenda for Visual Analytics" Other sources include:

1) Designing the User Interface, 5th Edition, B. Shneiderman & C. Plaisant, Addison Wesley (2010).   Chapter 14.  Two sample chapters at 
2) Readings In Information Visualization: Using Vision to Think,
      Stuart K. Card, Jock D. Mackinlay, and Ben Shneiderman,
      Morgan Kaufmann Publishers, San Francisco, January 1999, 686 pages, ISBN 1-55860-533-9, 

Previous Class pages:

Other University Class pages:


Berkeley CS:

Berkeley iSchool:

Analyzing Big Data with Twitter

Data Visualization

Social Data Revolution

Univ of British Columbia:

VA Tech:

GA Tech:

Info Vis in Info Science (iConference Best Paper)


I appreciate the provision of:

-- Spotfire from Tibco for use by this class, based on the long history of collaboration that goes back to the initial development of Spotfire at the University of Maryland.

-- Tableau's data visualization software courtesy of the Tableau for Teaching program.

Getting started