Analysis, design, and implementation considerations for creating interactive data visualizations.
By the end of the course, students are expected to:
- Analyze interactive visualizations in terms of approaches to handling complexity: dynamic change over time, partitioning into multiple views, data reduction within a single view, and the derivation of new data.
- Design new interactive visualizations for complex datasets.
- Implement interactive visualizations using existing toolkits and libraries.
- Explain the trade-offs of using animation vs juxtaposed views vs derived data.
- Explain and justify methods to validate visualization design effectiveness including computational benchmarks, field studies on deployed software, and qualitative discussion of visual results.
| Lecture | Topic |
|---|---|
| 1 | Introduction |
| 2 | Vis Principles and Best Practices |
| 3 | Shiny (Guest Lecture by Vincenzo) |
| 4 | Interactivity - Manipulate |
| 5 | Interactivity - Facet |
| 6 | Interactivity - Reduce + Aggregate + Derive |
| 7 | Scalability |
| 8 | Usability + Review + Inspiration |
- "Visualization Analysis and Design", by Tamara Munzner
- Covers vis theory in detail
Please see the general MDS policies.