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Visual Analytics Course

Course lectures & Assignments

Course program

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.

Network data

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

High-dimensional data

Text and documents

Time series

Spatial data

Clustering & Aggregation

Clustering & Aggregation

  • High-dimensional data and Ward & Hyugen's principle
  • Self-organizing maps
  • Graph clustering

Other resources

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

Code examples

/net/html/perso/melancon/Visual_Analytics_Course/data/attic/start.1353058820.txt.gz · Last modified: 2012/11/16 10:40 by melancon