Data is everywhere in our society, or to put it differently, everything is data from scientific research to everyday human activity. The ability to understand and communicate data is becoming an essential skill in this era of big data. Visualization leverages our visual perception to provides a powerful yet accessible way to make sense of large and complex data. In this course, we will learn basic principles on how to design effective visualization for data analysis and communication. To get your hands dirty, we will also have hands-on workshops on using Tableau Public to visually explore data and tell data-driven stories.
No programming required!
Nam Wook Kim
2:00 - 5:30 pm
January 23th (Wed) and 24th (Thu), 2019
Maxwell Dworkin G125 Watjen Executive Seminar Room
** Slides are not available yet **
This class will provide a general introduction to visualization. What is visualization? Why is it important? What are different functions of visualization? We will answer these questions using historical examples.
What are general rules of thumb for good visualization design? This class will introduce several design principles for effective visualization design through diverse examples of good, bad and weird visualizations.
Well-designed visualizations leverage human visual perception to amplify our limited cognitive abilities. We will cover a set of perception theories on colors, size estimation, preattentive processing, gestalt principles, and more.
To visualize data, we need to understand the properties of data and the types of datasets. How can we encode the data into visuals? We will also learn fundamental visual encoding channels and their effectiveness rankings by data types.
The philosophy of exploratory data analysis lies is the iterative process of formulating analysis questions, exploring data (mostly graphically) to uncover its underlying structure, and discover new insights. We will use Tableau Public to practice exploratory data analysis on a set of sample datasets.
Early visualization research focused on supporting data exploration and analysis tasks. Visualization is now widely used in the wild to communicate data and messages to a general audience. This class will examine what makes visualization communicative through examples and introduce storytelling with data.
We will recap what we learned and conclude the course by taking a look at advanced topics in visualizations, including maps, networks, high-dimensional data, text, evaluation, interaction, animation, and visual analytics.
The course material is based on CS 171 taught by Hanspeter Pfister at Harvard, as well as other visualization classes taught by Jeffrey Heer at the University of Washington, Maneesh Agrawala at Stanford University, Alexander Lex at the University of Utah, and Tamara Munzner at the University of British Columbia.