This course is designed as a lecture/seminar dealing with data visualization from a largely psychological and historical perspective.
Information visualization is the pictorial representation of data.
This course will examine a variety of issues related to data visualization from a largely psychological perspective, but will also touch upon other related communities of research and practice related to this topic:
We will consider visualization methods for a wide range of types of data from the points of view of both the viewer and designer/producer of graphic displays.
Grades will be based on the following components:
Discussion leader (20%) Each session, 1-2 of you will lead a brief discussion on one of the readings, sub-topics or an application related to the topic. (~ 5 min.).
Class presentation (40%) In the last week, each person will give a ~ 20-25 min presentation on a topic of research, application, or software related to data visualization. The topic is not restricted to things covered in the lectures. You’ll find a few random suggested topics on the Resources page, but you can also get a sense of the range and scope of projects from those listed on the Students page
You should prepare a brief topic proposal and send it to me by email by the end of Reading Week, Feb. 26. This is ungraded, but intended to help you shape your topic.
Your in-class presentation should be accompanied by a slide show or other visual materials. It would be best to post your presentation materials in advance, preferably by a link to cloud storage or as an email attachment to the class listserv.
Evaluation of the presentation will be done by peer review as well as by the instructor. A rating form will provided.
Research proposal (40%) Prepare a brief research proposal (~ 5-8 pages) on a data visualization topic. This can an empirical study of some aspect of data visualization, an historical review, or a proposal for development or implementation of a graphical method. The topic should not be identical to that of your presentation. This will be due on Friday, April 22, the week following the last class.
The lecture slides, tutorials and R scripts linked here are available under a Creative Commons Attribution-NonCommercial-ShareAlike license. They are available to everybody under the terms of this license and can be shared.
All other materials, notably course videos and support material files, should not be copied beyond your personal machines and hence are not available for redisitrbution.
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