Prerequisites
ARST 575H: completion of the MAS core courses for MAS students
LIBR 515: None
Dual students must meet the prerequisites for the section [ARST or LIBR] in which they are registered.
Note: Knowledge of advanced mathematics is not required for this course.
Course Overview
This course provides an overview of the fields of Information Visualization and Visual Analytics. The goal of Information Visualization is to harness visual perception strengths, graphic design principles, and computational power to create effective and ethical designs that allow people to explore, discover, and communicate insights from large datasets. Visual Analytics emphasizes human analytical reasoning facilitated with interactive information visualizations to synthesize information and derive insights from massive, dynamic, ambiguous, and often conflicting data; detect the unexpected; provide timely defensible and understandable assessments; and communicate assessment effectively for action. Emphasis in this course will be placed on Information Visualization design and on Visual Analytics for exploration, discovery, and communication of data-based insights to specific audiences.
Learning Outcomes
Upon completion of this course students will be able to:
- Select, organize, and identify strengths and biases on datasets to be used for visual analyses and design of information visualizations.
- Analyze, describe, and classify information visualizations based on a variety of visual, physical, contextual, and interpretive attributes.
- Design interactive visualizations of data informed by graphic design principles and information visualization theory.
- Critically evaluate information visualization designs based on their expressiveness, effectiveness, ethics, and empathy.
- Use software to create interactive information visualizations and data-based infographics that satisfy information needs.
- Plan and implement information visualization and visual analytics projects to tell compelling stories about data to specific audiences.
Topics
- History, concepts and principles of information visualization and visual analytics
- Understanding, structuring, and evaluating the base data
- Theories of human visual perception and cognition
- Basic graphic design principles for the visual representation of data
- Transforming raw data into visualizations
- Interaction techniques for data visualizations
- Tools for designing information visualizations
- Information visualizations for structured (i.e., tabular, geographic, and network) and unstructured data (i.e., text and documents)
- Infographics for evidence-(data-)based visual communications to general audiences
- Ethical, inclusive, anti-racist, and do-not-harm design of visual representations of data
- Critical evaluation of information visualization designs