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mation graphics and visual systems. More specifically, the learning outcomes for
the course include
- Students should gain an in-depth understanding of the field of Information
Visualization including key concepts and techniques.
- Students should be able to critique visualization designs and make sugges-
tions to improve them.
- Students should be able to design effective visualization solutions given new
problems and data domains.
- Students should learn about the spectrum of commercial system solutions
available in this area and how to choose one for a particular task or problem.
Perhaps the main challenge that I have faced in this course over the years is to
construct a coherent syllabus and flow of topics throughout the term. Information
Visualization is still a new area that is growing and maturing. Consequently,
it does not exhibit a well-understood and agreed-upon set of topics that flow
smoothly from one to the next. In my experience teaching the course, a number
of key ideas have risen to the surface and I make these the important components
of the course:
- Data foundations - A description and model of the different types of data
that are encountered and how this data is transformed and stored for easier
subsequent manipulation.
- Cognitive issues - A discussion of the user's goals and tasks in using an
information visualization system. What cognitive benefits can visualization
provide?
- Visualization techniques - A description of the different visual represen-
tations and interaction techniques that have been invented.
-Interaction - A discussion of the different types and the many issues sur-
rounding interaction.
- Data types/structures - An introduction to specific types of data (e.g.,
time series, hierarchical, textual) and the visualization techniques that are
well-suited at representing those data types.
- Data domains - An examination of different domains (e.g., software engi-
neering, social computing, finance and business) and the visualization tech-
niques that are helpful to people working in those areas.
- Evaluation - A dialog about the challenges of evaluation in information
visualization and a review of different evaluation techniques that have been
used in the area.
Some of these topics are fundamentally interwoven so the flow of concepts is
not clearly self-contained and independent. For instance, certain visualization
techniques are best used for specific data types (e.g., treemaps for hierarchical
data). In organizing the course content, I feel this tension and often struggle
with which topics to teach first. Nonetheless, my course uses this progression of
topics as its organizational framework.
The course is lecture-based but I try to engage the students in discussions
about the different concepts being studied. I have used Bob Spence's textbook
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