This course aims to teach learners how to develop design spaces for visualization. The learning outcomes include understanding the concept of design spaces, creating design spaces with multiple axes, characterizing narrative and perceptual combinations, and assessing the descriptive power of design spaces. The course covers skills such as chart combinations for heterogeneous data, making enhancement choices on base chart types, and wrangling multi-table data. The teaching method involves lectures on various design space concepts and practical examples. The intended audience for this course includes data visualization professionals, designers, researchers, and anyone interested in creating effective visualizations.
Stanford Seminar - Developing Design Spaces for Visualization - Tamara Munzner
Stanford University via YouTube
Overview
Syllabus
Introduction.
Design spaces: Continuing theme.
Design spaces: What are they!.
Design spaces: How to create!.
Design space with three axes.
Combinations: Characterize narrative, perceptual.
Viable combinations.
Assessment & adoption descriptive power.
Domain prevalence design space.
By the numbers.
Design space axis: Chart combinations of heterogeneous data.
Design space axis: Enhancement choices, atop base chart types.
Process overview.
Key finding journalists use many, many tables.
Multi-table data wrangling design space.
Assessment: Cross-check.
Task abstraction: Gap.
Mapping terms.
Summary: Multiple design spaces.
Design spaces: How to assess? Larger context theory types.
Taught by
Stanford Online
