3 credits
Spring 2025 Lecture Upper Division
This course provides a systematic, comprehensive framework to study principles, design choices, and development toolkits on information visualization design and development. It focuses on creating innovative and intuitive visualization solutions to provide users with peripheral awareness of meaningful information from a complex data set. Employing a user-centric approach, this course studies and analyzes visualization design and development from data, tasks, and visualization and interactions. The students will learn perception theories on visualization and use visual elements such as space and color to encode data, compare, evaluate, choose among different visualization forms and interaction methods. Through a project-based learning approach, students will conduct case studies on various visualization designs, analyze the user, data, tasks, and design choices in the visualizations, and finally design and develop information visualization solutions as course projects. Students should have finished their undergraduate study in domains that are closely related to one or more of the following: business analysis, statistics, knowledge management, computer science, software engineering, interaction design, human-centered computing, scientific visualization or information visualization. Permission of department required.
Learning Outcomes1Describe the purposes of visualization and the process of design and developing visualizations.
2Explain visual encoding channels, visualization design rules, and design principles using scientific theories on visual perception.
3Apply popular visualization forms to represent different types of data.
4Analyze visualization designs to differentiate the functions and purposes of visual elements and channels
5Evaluate different visualization forms, arrangements, and interactions about their pro and cons, purposes, and suitable contexts.
6Design, develop, and evaluate an innovative visualization system with interlinked multiple views to represent a large complex dataset.
Restrictions
made by Purdue students.