Office hours: Tu Thu 4-5pm (Live Zoom, piazza discussion boards)
Phone: 2-1524 (leave message, but better send email)
TA: please see pinned postings on Blackboard and Piazza
Meeting time and venue:
TuTh 7:00-8:20pm (online, synchronous, Live lecture with Q&A)
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
- "Interactive Data Visualization: Foundations, Techniques, and Applications, Second Edition" by M. Ward, G. Grinstein, and D. Keim, 2015
- "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.
- "Visualization Analysis and Design" by Tamara Munzner, AK Peters, 2014.
- "Now You See It: Simple Visualization Techniques for Quantitative Analysis" by Stephen Few, Analytics Press, 2009.
- "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.
- "Real-Time Volume Graphics" by K. Engel et al. AK Peters, 2006.
Lab assignments: 30% (MOSS for code plagiarism checks)
Midterm exam: 30% (Live Zoom proctoring)
Final exam: 40% (Live Zoom proctoring)
- Project 1 (5%): Find a sufficiently complex dataset about a topic you find interesting. Ideally you would find multiple datasets that address a common topic but from different viewpoints and aspects. Then you would fuse them together to gain more explanatory power.
- Project 2 (5%): Preprocess the dataset from project 1 and implement some first interactive visualizations. Make a demo video and write a report.
- Project 3 (5%): Implement some more advanced data processing and interactive visualization algorithms. Make a demo video and write a report.
- Project 4 (5%): Interlude project on scientific visualization of volumetric data with a spatial context, using a public domain software package. Make a demo video and write a report.
- Project 5 (10%): Make a complete dashboard of brushable interlinked and interactive visualizations that show the different aspects of your data, and the relations among them, in a compelling way and allow insightful data explorations. Make a demo video and write a report.