Information Visualization
Instructor: Dr. Ben Rydal Shapiro
TAs: Cody O'Donnell, Arpit Mathur, Raveena Shah
Course Overview
An introduction to data visualization techniques from graphic design, computer science, architecture, semiotics, psychology, cartography, and cognitive science. Students apply techniques through team design projects developing interactive visualizations of real-world datasets. Format includes interactive lectures, discussions, design challenges, and assignments.
Target Audience: Students exploring visualization applications (HCI, data science, education, research) and those building computational/visualization tools.
Prerequisites: No formal prerequisites; basic knowledge of graphics tools (D3.js, Processing, p5.js, Vega, HTML5) and data analysis tools (Python, Excel, Matlab) expected.
Learning Goals
- Learn visualization techniques from multiple disciplines
- Develop interactive visualizations for real datasets using D3.js
- Address specific problems or user needs through design
- Explore visualization in HCI, data science, education, and research
- Build computational visualization tools and systems
Grading
Individual Assignments (33%)
- Data Exploration & Analysis (5%)
- Critiquing Commercial Visualization System (10%)
- Personal Data Collection & Visualization (10%)
- Programming Assignments (8%)
Team Project (40%)
- Design Review I: Proposal & Task Analysis (5%)
- Design Review II: Concepts & Prototypes (10%)
- Design Review III: Final Presentation & Tool (25%)
Participation (15%)
Attendance, Canvas posts, peer evaluations
Summary Exam (12%)
In-class exam
Course Schedule
Week 1
Week 2
Week 3
Week 4
Week 5
- Data Humanism - Giorgia Lupi
- How can we find ourselves in data (TED Talk)
Week 6
Week 7
Week 8
- The Eyes Have It - Shneiderman
- The Death of Interactive Infographics?
- In Defense of Interactive Graphics
Week 9
- Re-read: The Eyes Have It
Week 10
Spring Break
Week 11
Week 12
Week 13
Required & Recommended Materials
- Required: Sketchbook with no lines
- Recommended: Interactive Data Visualization for the Web, 2nd ed. by Scott Murray
- Recommended: Envisioning Information by Edward Tufte
- Recommended: Fullstack D3 and Data Visualization Guide by Amelia Wattenberger
Key Readings
- Touring the Visualization Zoo
- The Eyes Have It: A Task by Data Type Taxonomy - Shneiderman
- Chapter 1: Graphical Excellence - Edward Tufte
- Data Humanism - Giorgia Lupi
- Narrative Visualization - Segel & Heer
- Data is Personal
- 39 Studies About Human Perception in 30 Minutes
- D3: Data-Driven Documents
- Can Data be Human? - The New Yorker
- An Emotional Response to the Value of Information Visualization
- Effectively Communicating Numbers - Stephen Few
- Data Visualization for Human Perception
- Color Use Guidelines for Data Representation
- Modeling Color Difference in Visualization Design
- On Graphonyms: The Importance of Chart Type Names
- When Charts Go Weird: The Joy of Xenographics
- Exploration and Explanation in Data Driven Storytelling
- Reinventing Explanation - Michael Nielsen
Video Resources
Team Project
Teams of 3-4 members work throughout the semester to select or collect a dataset, develop an interactive visualization addressing a specific problem, and implement it using D3.js or comparable language/library. Three design reviews provide structured feedback.
Software & Tools
Programming & Visualization
Commercial Tools
Data Preparation
Color & Chart Tools
Visualization Blogs & Resources
Historical & Inspiration
- Edward Tufte
- Envisioning Information - Tufte
- W.E.B. Du Bois Data Visualizations - NAACP History
Additional Resources
Acknowledgements
Course integrates ideas from John Stasko, Jeffrey Heer, Sheelagh Carpendale, Alberto Cairo, Danielle Szafir, Alex Endert, Hadley Wickham, Jennifer Kahn, and others in information visualization and education.