Modules

We introduce a broad definition of visualizations as solutions to user data tasks and consider how they can be effective at this. We’ll use the basic notion of “what does a visualization make easy to see” to develop intuitions about what makes visualizations effective (good). We’ll work out course mechanics.

We will look at how we can consider visualizations in terms of the building blocks of data and task abstractions and visual encodings. We will see how this allows designing and analyzing visualizations. We will practice design process (including critique). Students will begin to design and analyze visualizations (pen-and-paper).

We will look at strategies for implementing (creating) visualizations and how visualization can be used for exploring data (in addition to communicating stories with it). We will focus on implementation and scale. Students will practice creating and critiquing visualizations.

We will be more explicit in how we evaluate visualizations and visualization research. We will provide more formal approaches to considering aspects of design and analysis. Students will create and critique visualizations.

We will explore the design, perceptual, cognitive, and data foundations in more depth. We will examine how the science can influence design and practice. Students will apply these ideas to analyze data and create visualizations.

We will look at some standard “hard problems” that come up including hard data properties (e.g., uncertainty, scale), data types (e.g., networks, high-dimensional records), and applications. We will look at some of the standard approaches to address these problems. The design exercises will give more practice at creating and critiquing visualizations.

We will look at emerging themes in visualization to see where visualization research is providing new solutions. We will look at design and research process in visualization.