Overview
This course introduces the principles and practice of visualizing data for exploration, analysis, and sensemaking. Students will learn how human perception and cognition shape visual analysis and design, and how to choose appropriate visual encodings for different data types. Topics include visualization of tabular, network, temporal, and spatial data; interaction techniques; dashboard design; and evaluation. Emphasis is placed on using visualization to build intuition about data and models to reveal patterns, surface anomalies, and making model behaviour interpretable.
Schedule
| Week | Topic | Deliverable |
|---|---|---|
| 1 | Visualization Fundamentals | |
| 2 | Introduction to Data-Driven Programming | A1 released |
| 3 | Data & Task Abstraction | A1 due, A2 released |
| 4 | Memory, Attention & List Alignment | A2 due, A3 released |
| 5 | Interactivity 101 | A3 due |
| Midterm Exam | ||
| 6 | Matrix Alignment | Project Released, A4 released |
| 7 | Scalar and Vector Fields | A4 due, A5 released |
| 8 | Networks and Trees | A5 due, A6 released |
| 9 | Parallel & Spatial Layouts | A6 due |
| 10 | Re-Thinking 3-D | |
| 11 | Aggregation & Clustering | Project Progress Check-in |
| 12 | Advanced Topics | |
| Endterm Exam | ||
| 13 | Project presentations | |
| Project due | ||
Note: This schedule is tentative and subject to change. Last updated: 2026-07-24.
Tutorials
Weekly tutorial will be posted on the courses GitHub page. Please reach out to the Teaching Staff with your email id to get added to the repository.
Assessments
No final exam. Project may be done in groups of 2 or 3. All assessments managed through Avenue.
Teaching Assistant
Details coming soon