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Class Organization


sidehead Old Pages (2022) * Tutorials ** Tutorial 1 ** Tutorial 2 ** Tutorial 3 and 4 ** Tutorial 5 ** Tutorial 6 * Assignments Overview ** Assignment 0 ** Assignment 1 ** Assignment 2 ** Assignment 3 ** Assignment 4

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Class overview


As the amount of data is growing faster than the speed of computers to process them, it becomes harder to analyze this data, to understand it both at a global level and at a smaller scale, and to make decisions based on the data. Visualization turns data into visual representations that allow users to understand it and to provide them with interactive tools that are designed to efficiently navigate and analyze these representations. The class introduces students to the field of visualization, discusses various types of visualizations according to the type of data being analyzed (tabular data, hierarchical data, graphs, texts, 3D data), and teaches the process to build data analysis tools.


Each lecture block comprises a 1h30' lecture and a 1h30' lab. The lectures introduce basic concepts, while in the labs the students do practical exercises and are introduced to the assignments they complete in-between meetings.


  1. General class introduction, introduction to information visualization
  2. Perception and color
  3. Multi-dimensional data visualization
  4. Graphs and trees
  5. Time-dependent data
  6. Interaction
  7. Storytelling with data
  8. Text visualization

Learning objectives

  • to understand the need for visualization to make sense of data
  • to understand different data types and their visualization needs
  • to understand the implications of the human visual system for visualization
  • to get to know different types of visual representations
  • to understand the need for and potential of interactivity for visualization
  • to get practical experience through a group project, developing a visualization tool for a self-chosen dataset


There is no official prerequisite for this course. However, to be successful in the class the students are expected to:

  • have basic knowledge of programming,
  • be able to work with basic data tables (CSV) using standard tools such as MS Excel or LibreOffice, and
  • be able and willing to learn a new data processing/visualization API, and independently debug code and solve issues using online resources.
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