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CS525D - Data Visualization - Fall, 2011
Prof. Matthew Ward
FL231, 831-5671, firstname.lastname@example.org
Office Hours: Tuesday and Thursday at 2, Friday at 1,
others by appointment
Visualization is the graphical communication of data and information for the
purposes of presentation, confirmation, and exploration. For thousands of
years, images have been used to convey numbers, concepts, and relationships
using techniques such as maps, icons, graphs, and other visual forms. In the
past 2 decades, visualization has evolved into a discipline, drawing from such
fields as graphics, human-computer interaction, perceptual psychology, and
The goal of this course is to expose students to the field of data
visualization and familiarize them with the stages of the visualization
pipeline, including data modeling, mapping data attributes to graphical
attributes, existing visualization techniques, tools, and paradigms, perceptual
issues, and evaluating the effectiveness of visualizations for specific data,
task, and user types.
Interactive Data Visualization: Foundations, Techniques, and Applications, by
M. Ward, G. Grinstein, and D. Keim. ISBN 9781568814735.
Additional Resources: All documents for the course will be made available
at the website
http://www.cs.wpi.edu/~matt/courses/cs525D/. Also, I
extensive library of books and conference proceedings on visualization (see the
list below). If there is any topic that you'd like to delve deeper into, or
look for clarification or alternate viewpoints, feel free to borrow any of my
collection for a week or two.
Each week the assignment will consist of several components, each with an
expected amount of time you should dedicate:
- Reading: Each week we will focus on a different chapter of the book. I
would expect the reading to require 2-3 hours per week.
- Exercises: I would like each of you to spend up to 1 hour per week
exercises listed at the end of each chapter. As I'm not sure how long each
take, I suggest doing as many as you can within the 1 hour expectation. You
don't have to do them in the order they are listed.
- Projects: I would like each of you to spend up to 5 hours per week
trying the exercises listed at the end of each chapter. As I'm not sure how
long each should take, I suggest doing as many as you can within the 5 hour
expectation. You don't
have to do them in the order they are listed.
- Extra Credits: There are several ways to obtain extra credits for this
course, mostly revolving around suggesting additions and corrections for the
For example, you can suggest additional exercises and projects for a given
For these, I would expect you to also provide a potential answer/solution.
is to suggest additional references or related readings. For these, I would
that you provide the complete reference (bibtex format would be best), along
where it should be added to the book and (briefly) why it should be considered
inclusion. Finally, finding bugs in the book and sending corrections to me is
another way to earn extra credits.
I will provide each student with a 2GB USB memory stick for turning in your
assignments. You should create folders based on the chapter you are working
Within those folders you should create files or subdirectories for each of your
homework components. To better help with planning for this course (current and
future versions) I'd appreciate it if you included, for each component, how much
time you actually spent on it. The sticks will be collected every 2 weeks and
returned on the following week.
There will be no exams given for this course.
For the second half of the semester, the weekly programming projects will be
replaced by a single term (really half term) project. The steps of this
project are as follows:
- Select some socially relevant data set or information source as
a focus for visual analysis. Confirm your topic with Prof. Ward.
- locate and summarize 1 or 2 papers that present methods for visualizing
this kind of data. Include complete references in your summary.
- Design or extend a visualization to allow exploration of your
data/information. You are not allowed to just use Excel! There should be some
- Explore your dataset and identify a modest number of "interesting"
features in the data.
- Write a short (less than or equal to 8 pages, single spaced) paper
describing the data, the papers you read related to visualizing this type of
data, the process you followed in developing your visualization, the methods
used for exploration, and the things you discovered. Include screen shots
and relevant references.
This project is due by the start of our last class. Assuming we have time,
students will be given the opportunity to present their results.
Your grade will be based on bi-weekly reviews of your homework from the chapters
we've covered. For each review, I will assign a letter grade, and your final
grade will be an average of these grades. The rough grading will be as follows:
check/B = met expectations, check+/A = exceeded expectations, check-/C =
did not meet expectations. Late assignments without prior permission may
have a negative impact on the grade.
Copying the work of others and turning it in as your own is considered academic
dishonesty, and is strictly forbidden in this class. Violators of this policy
will receive a 0 grade for the assignment, and the incident will be reported to
the department chair and the Dean of Students' Office.
You can use whatever computer you have at your disposal, as long as it supports
at least 256 colors and your programs can be demonstrated on a machine on
OpenGL, Java2D, Java3D, or X can be used for software development. Basically
whatever language you used in your graphics course will do. In most cases,
you can get by with 2-dimensional graphics, though for some types of
visualization, 3-D is essential. When you turn in your assignments, please
include instructions for compiling and executing the program. I may decide
to instead have you demonstrate the programs in action if it is too
time-consuming for me to figure out how to build and run them.
It may also be possible to build your assignments using an existing
visualization tool as a base. Some
visualization tools that you can download and test include:
- XmdvTool -
- SpiralGlyphics -
- OpenDX -
- Prefuse -
- DeVise -
- VTK -
- CViz -
- VolVis -
- extra points for finding others (other than WPI-developed)
Books Available from Prof. Ward:
- Bartz, Dirk, Visualization in Scientific Computing '98, Springer, 1998.
- Bederson, Ben, and Shneiderman, Ben. The Craft of Information
Visualization, Morgan Kaufman, 2002.
- Berthold, Michael, and Hand, David, Intelligent Data Analysis (2nd edition),
- Brown, Judith. et al., Visualization: Using computer graphics to explore
data and present information, Wiley and Sons, 1995.
- Card, Stuart, et al.. Readings in Information Visualization, 1999.
- Chen, Chaomei. Information Visualization and Virtual Environments.
- Chen, Chaomei et al., Handbook of Data Visualization, Springer, 2008.
- Cleveland, William, Visualizing Data, Hobart Press, 1993.
- Di Battista, Giuseppe et al., Graph Drawing, Prentice Hall, 1999.
- Diehl, Stephan, Software Visualization, Springer, 2007.
- Fayyad, Usama, e. al.. Information Visualization in Data Mining and
Knowledge Discovery. Morgan-Kaufmann, 2002.
- Few, Stephen, Show Me the Numbers, Analytics Press, 2004.
- Friendly, Michael, Visualizing Categorical Data, SAS Publishing, 2000.
- Grave, Michael, et al., Visualization in Scientific Computing, Springer-Verlag,
- Hagen, Hans, et al., Scientific Visualization - Dagstuhl '97, IEEE CS Press, 2000.
- Harris, Robert. Information Graphics, a Comprehensive Illustrated
Reference, Oxford University Press, 1999.
- Keller, Peter, and Keller, Mary. Visual Cues: Practical Data
Visualization. IEEE Press, 1993.
- Kerren, Andreas, et al.. Information Visualization: Human-Centered Issues and
Perspectives, Springer, 2008.
- Kosslyn, Stephen. Elements of Graph Design, W.H. Freeman, 1994.
- Lichtenbelt, Barthold, et al. Introduction to Volume Rendering.
- Mullet, Kevin, and Darrell Sano, Designing Visual Interfaces, Prentice
- Nelson, Gregory, et al.. Visualization in Scientific Computing. IEEE
CS Press, 1990.
- Nelson, Gregory, et al.. Scientific Visualization: Overviews,
Methodologies, Techniques. IEEE CS Press, 1997.
- Post, Fritz et al., Data Visualization: the state of the art, Kluwer,
- Schroeder, Will, et al.. The Visualization Toolkit (2nd edition).
- Soukup, Tom, and Davidson, Ian, Visual Data Mining, Wiley, 2002.
- Spence, Robert. Information Visualization. Addison-Wesley, 2001.
- Stasko, John, et al., Software Visualization, MIT Press, 1998.
- Telea, Alexandru, Data Visualization Principles and Practice, AK Peters,
- Thalmann, Daniel, Scientific Visualization and Graphics Simulation, Wiley, 1990.
- Thomas, James, and Cook, Kristin. Illuminating the Path: the Research
and Development Agenda for Visual Analytics, IEEE CS Press, 2005.
- Tufte, Edward. The Visual Display of Quantitative Information.
Graphics Press, 1983.
- Tufte, Edward. Envisioning Information. Graphics Press, 1990.
- Tufte, Edward. Visual Explanations. Graphics Press, 1997.
- Tufte, Edward. Beautiful Evidence, Graphics Press, 2006.
- Ware, Colin. Information Visualization: Perception for Design.
- Wilkinson, Leland, The grammar of graphics (2nd edition), Springer, 2005.
- Woolman, Matt. Digital Information Graphics, Watson Guptill Publishers,
- Proceedings of IEEE Visualization Conference. 1990 - present.
- Proceedings of IEEE Symposium on Information Visualization. 1995 -
- Proceedings of IEEE Symposium on Visual Analytics Science and Technology,
- Proceedings of International Conference on Information Visualization.
- Proceedings of the Eurographics Visualization Symposium. 2003, 2004.
- Proceedings of Volume Visualization and Graphics Symposium. 1998, 2000,
- Proceedings of Parallel Visualization and Graphics Symposium. 1999.
- Proceedings of Parallel and Large-Data Visualization and Graphics
- august 30:
- introduction and foundations
- september 6:
- data models and preprocessing
- septemer 13:
- perceptual issues
- september 20:
- visualization frameworks and taxonomies
- september 27:
- spatial data visualization techniques
- october 4:
- geovisualization techniques
- october 11:
- non-spatial data visualization techniques
- october 18:
- trees and graphs
- october 25:
- no class (visweek conference)
- novembe 1:
- text visualization techniques
- november 8:
- interaction concepts
- november 15:
- interaction techniques
- november 29:
- designing effective visualizations
- december 6:
- evaluating visualizations
- december 13:
- future directions
- National Center for Health Statistics -
- National Archive of Criminal Justice Data -
- StatLib at CMU -
- Links to more statistics datasets -
- Everything about baseball -
- Weather data -
- UC Irvine KDD Archive -
- Inter University Consortium for Political and Social Research -
- InfoVis and Vis conference contest data sets - conference web sites
- Border bouncing data from NVAC - see Prof. Ward
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