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Labs | Handouts | |
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| 01/27 | Intro and logistics | intro | ||
| 01/29 | Basic visualizations and tasks, data types, examples, ethical considerations |
Ward: chapter 1 | basicTasks | |
| 02/03 | Data preparation (cleaning, imputation, data set integration) |
Ward: chapter 2 | dataPrep | |
| 02/05 | D3, AI-assisted coding for VIS applications (design, debugging, refactoring) |
see echo 360 recording for AI-coding demo | lab1 | d3 |
| 02/10 | Big data and data reduction (distance/sim metrics, intro to clustering) |
Aggarwal 2.4.3.1, 2.4.1, 6.3.1 | dataRed | |
| 02/12 | High-D data: concept, subspaces, dimension reduction, PCA | see above | dimRed | |
| 02/17 | Cluster analysis: hierarchical, density, model, embedding, temporal |
Aggarwal 6.4-5 | cluster | |
| 02/19 | Perception and cognition (human visual system, color, contrast) |
Ward: chapter 3 | lab2a | percept |
| 02/24 | no class (snow day) | |||
| 02/26 | Visual design and aesthetics |
Ward: chapter 4, Munzner: chapter 5 | design | |
| 03/03 | Visualization of multivariate and high-D data: linear methods, projections |
Ward: chapter 8 | high-D-Vis-lin | |
| 03/05 | Vis. of multivariate and high-D data: non-linear methods, embeddings | Ward: chapter 8 | high-D-Vis-nlin | |
| 03/10 | Visualization and AI: mutual support and capabilities (VIS4AI, AI4VIS) | lab2b | Vis+AI | |
| 03/12 | Principles of interaction: drive what is visualized, analyzed & how |
Ward: chapter 11 | interact | |
| 03/17 | no class (Spring Break) | |||
| 03/19 | no class (Spring Break) | |||
| 03/24 | ||||
| 03/26 | Midterm 1 | |||
| 03/31 | ||||
| 04/02 | ||||
| 04/07 | ||||
| 04/09 | ||||
| 04/14 | ||||
| 04/16 | ||||
| 04/21 | ||||
| 04/23 | ||||
| 04/28 | ||||
| 04/30 | ||||
| 05/02 | ||||
| 05/07 | Midterm 2 | in Engineering 143 and 145 | ||
| 05/12 | 8:30-11:00 pm: Final Project presentations |