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For more information on these pages, or to report an error please contact Guy Melançon

Introduction: this first course presents the fields, and roughly follows Ward *et al*.'s first chapter.

- Examples
- Munzner's nested model
- The visualization pipeline

Perception: visualization is about perceiving features and structures in images.

- It's all about distance
- InfoVis: a few definitions
- Visual features that matter

Data: data processing occurs ahead of visualization but nevertheless remains an important aspect, and preliminary phase, to the visualization process.

- Histograms
- Normalization

Graph basics: we shall be concerned with networks – a lot. This course makes sure everyone gets familiar with basic graph theory.

- Graphs and subgraphs
- Paths, connectedness and trees
- Partitions and quotient graphs
- Traversals
- Weighted graphs

Visual Analytics with Tulip: Tulip is the framework we shall be using. This course also makes sure everyone is familiar with python programming.

- Trees
- Hierarchical graphs and planar graphs
- General graphs
- Network metrics
- Bipartite graphs

- Parallel coordinates

- Self-organizing maps

See here for a list of definitions and notations used throughout these pages.

Bibliography and External Links (Access this page, edit it and add your own suggestions.)

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