Visualization is a co-listed 4th year undergraduate and graduate course that focuses on graphical techniques for data visualization that assist in the extraction of meaning from datasets. This involves the design and development of efficient tools for the exploration of large and often complex information domains. Applications of visualization are broad, including computer science, geography, the social sciences, mathematics, science and medicine, as well as architecture and design. The course will cover all aspects of visualization including fundamental concepts and the role of human perception.

• Current Offering: Winter 2013!
  
• Instructor: Dr. Stephen Brooks
• Class Location: COMPUTER SCIENCE LAB 2
  
• Class Time: 10:35-11:55am, Mondays & Fridays
  
• Office Hours: Monday, 1:00-2:00pm, Room 327
  

• Strongly Recommended:
  Information Visualization: Perception for Design by Colin Ware, Morgan Kaufmann, 2012.
  
  
• Recommended:
  Interactive Data Visualization for the Web: An Introduction to Designing With D3, Scott Murray, 2012.
  
  
• Recommended:
  Readings In Information Visualization: Using Vision to Think by Stuart K. Card, Jock D. Mackinlay & Ben Shneiderman, Morgan Kaufmann, 1999.
  
  
• Recommended:
  Information Visualization by Robert Spence, Addison Wesley, 2001.
  
  
• Recommended:
  Witzy's Block Party (Little Suzy's Zoo Series) by Suzy Spafford, Scholastic Publishers, 2001.
  

• Overview of information visualization and applications
• Models of visualization and knowledge representations
• Dimensionality (2D, 3D, volumetric, high dimensionality)
• Visual perception of data presentation, texture and color
• Navigation, scale and zooming
• Graph structures, trees and networks
• Visualization of data queries, data editing and customization of visual data
• Efficiency, contraints, occlusion, focus plus context, level of detail
• Realism vs. Non-photorealism
• Evaluation of visualization systems
• Applications of visualization

All work you submit must be your own. It is fine to discuss problems, but when it comes time to submit solutions, the materials you hand in must be done individually, by yourself. Any materials referenced must be attributed. All suspected instances of academic dishonesty must be reported to the Senate Discipline Committee. In particular, you should never show another student code that you have written for an assignment in this course, nor should you write code for another student to use in his/her assignment. Note that this specifically prohibits working with other students when writing the code for your assignments. As I said above, it is fine to discuss problems, but the code you submit must be your own, written by you alone. For further information regarding academic honesty at Dalhousie, please see the University plagiarism website.

Also note that all assignments and projects will be checked for plagiarism using automated software.

Late work will be penalized 5% per day (or part thereof). You will not receive credit for work that is more than 2 days late.