Academics / Courses / DescriptionsCOMP_SCI 333: Interactive Information Visualization
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Prerequisites
COMP_SCI 214Description
When well designed, visual displays of data capitalize on human facilities for processing information and improve comprehension, memory, inference, and decision making. Creating visualizations is getting easier thanks to a proliferation of digital tools for transforming and visualizing data. Yet the best visualizations are still often creating by skilled human designers who have considerable experience with design analysis.
In this course we will consider what it means for a visualization to be effective and what sorts of principles, techniques and algorithms from statistics, graphic design, databases, perceptual psychology, cognitive science and related fields can help us create effective visualizations. Though there are different uses of the term “visualization” in popular media, for the purposes of this class, we will treat information visualization as the use of interactive interfaces to visually represent abstract data. The course is targeted both towards students interested in using visualization in their own work, as well as students interested in building better visualization tools and systems. In addition to participating in class discussions, students will demonstrate their learning through weekly short quizzes and several assignments requiring visualization design, exploratory data analysis, and interactive visualization implementation.
In addition to being comfortable programming, students should have a basic working knowledge of, or willingness to learn, a graphics API. We we will cover introductory D3 in class to get you started, but this is not a JavaScript or web development course. Students will be expected to self-study to learn JavaScript and other basic web technologies (HTML, CSS) if they are not familiar with them.
- Formerly COMP_SCI 396/496.
- This course satisfies Interface breadth requirement.
COURSE COORDINATORS: Prof. Jessica Hullman
COURSE INSTRUCTOR: Prof. Kay or Prof. Jessica Hullman (Fall)