Visual Analytics

This course will give an overview of visual analytics as well as the overlapping fields of information and scientific visualization. Students will learn to programmatically process and analyze data with Python libraries widely used in statistics, engineering, science and finance. We will cover the design of effective visualizations. Students will learn to build data visualizations directly using a variety of data visualization libraries such as matplotlib, seaborn, and bokeh (Python) and interactive web-based visual analytics using JavaScript and D3. Project groups of students will each propose, design and build a visualization of a data set. The goals of the course are for students to: (1) Recognize the appropriate applications and value of visualizations; (2) Critically evaluate visualizations and suggest improvements and refinements; (3) Apply a structured design process to create effective visualizations; (4) Use programmatic tools to scrape, clean, and process data; (5) Use principles of human perception and cognition in visual analytics design; (6) Use visual analytics and statistics tools to explore data; and (7) Create web-based interactive visualizations.

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