This course will give students a foundation in the principles and practice of data visualization, particularly as applied to scientific and technical data. We will have weekly lectures, covering a wide variety of topics including human perception, color theory, principles of visual design, etc. We will also have weekly hands-on laboratory sessions in which students will have the opportunity to put the lecture material into practice.
Lab sessions will largely use the open-source R and ggplot tools, though others will be featured as well. Some assignments will be done via the DataCamp online learning platform.
This year, we will be adding additional emphasis on larger issues of scientific communication— after all, that's one of the main reasons we make visualizations!
Note: Topics and speakers may change through the term as we adjust our course plan.
|1||4/2/2018||Intro & baseline exercise||Slides|
We will be drawing material from a wide variety of sources for this course; as such, there is no single, required text book per se. However, we will frequently refer to Nathan Yau's Data Points, and highly recommend that anybody taking this class have a copy. Additionally, many lab sessions and some assignments will use exercises from Nathan Yau's Visualize This. Tamara Munzer's Visualization Analysis and Design is also incredibly useful.
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