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. 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!
We meet twice a week, alternating lectures on Mondays and labs on Wednesdays. Lab sessions will largely use the open-source R and ggplot2 tools.
Online, all the time
Mondays, 12:00 – 13:30
Wednesdays, 12:00 – 13:30
There will be regular homework assignments, typically as part of lab sessions. There will also be a final term project; more details about which will be forthcoming.
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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