Office hours: TBD (send email for
Meeting time and venue:
ABET course outcomes:
- An ability to transform spatial and non-spatial data from science, medicine, business, commerce, etc. into interactive visual representations.
- An understanding of the perceptual and cognitive reasoning processes that occur in humans when exploring visual artifacts derived from data to gain insight into the underlying phenomena.
- Working knowledge of principles and methods in human-computer interaction, data mining, computer graphics, and information visualization as applied to visual sense-making and data analytics.
- Practical experience with a number of popular public-domain data analysis and visualization packages and libraries.
CSE 219 (Computer Science III); AMS 210 or
MAT 211 (Linear Algebra), AMS 310 (Survey of Probability and Statistics)
Working knowledge in Java programming
- "Now You See It: Simple Visualization Techniques for Quantitative Analysis" by Stephen Few, Analytics Press, 2009.
- "Data Mining: The Textbook" by Charu Aggarwal, Springer, 2015.
For additional reference and on
reserve in the Science & Engineering library:
- "Visual Thinking for
Design" by Colin Ware, Morgan-Kaufman, 2008.
- "Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking" by F. Provost and T. Faucett, O'Reilly Media, 2013
- "Visual Computing for Medicine: Theory, Algorithms, and Applications" by Bernhard Preim and Charl Botha, Elsevier, 2013.
- "Computer Graphics: Principles and Practice -
Second Edition in C" by J. D. Foley, A. van Dam, S.K. Feiner, J.F.
Hughes, Addison-Wesley, 1995.
- "Visualization Toolkit" by W. Schroeder, K.
Martin, and W. Lorensen, 2nd ed., Prentice Hall, 1998.
- "Digital Image Processing" by R. Gonzales and R.
Wood, Prentice-Hall, 2002.
- "The Visual Display of Quantitative Information"
by E. Tufte, Graphics Press, 1983.
- "Envisioning Information" by E. Tufte, Graphics
- "Explanations: Images and Quantities, Evidence
and Narrative" by E. Tufte, Graphics Press, 1997.
Lab assignments: 30%
- Visual cluster analysis – use R for data analysis and D3.js for visualization
- Analysis and volume rendering of medical data – use an existing public-domain renderer to understand the volumetric image generation process
- Visual text mining – use R for data analysis and D3.js for visualization
- Visual analysis of large graphs -- use R for data analysis and D3.js for visualization
- Visual analysis of streaming, time-varying data -- use R for data analysis and D3.js for visualization