Syllabus for Scientific Visualisation

Vetenskaplig visualisering


  • 5 credits
  • Course code: 1TD389
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Computer Science A1N, Technology A1N, Computational Science A1N
  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2008-03-18
  • Established by:
  • Revised: 2022-02-14
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: Autumn 2022
  • Entry requirements:

    120 credits including Computer Programming I and Scientific Computing II. Scientific Computing II may be replaced by Numerical Methods and Simulation, 5 credits, Scientific Computing, Bridging Course, 5 credits, or Scientific Computing and Calculus, 10 credits. Proficiency in English equivalent to the Swedish upper secondary course English 6.

  • Responsible department: Department of Information Technology

Learning outcomes

On completion of the course, the student should be able to:

  • characterize the nature of datasets and the properties of the visual display medium; 
  • describe the data- and process-flow to transform data and information into visual representations;
  • explain visualisation methods and techniques for graphical representation of the most common types of data
  • use and program interactive software for various visualisation techniques;
  • assess computer-generated visualisations by drawing upon design principles and theories about the human visual system;
  • identify appropriate visualisation strategies and justify the chosen approaches.


Classification of data and properties of visual displays. The visualisation process pipeline including classical algorithms for rendering. Visualisation techniques for discrete and continuous data (scalar, vector, tensor fields) in 1D, 2D, and 3D spatial domains. Visualisation techniques for independent quantitative observations (amounts, proportions, frequencies, associations), data in multiple categories, and relations. Relevant aspects of human visual perception and cognition. Evaluation approaches in visualisation.


Lectures, laboratory work and compulsory assignments.


Written test (3 credits). Passed laboratory course and approved compulsory assignments are also required (2 credits).

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

Reading list

Reading list

Applies from: Autumn 2022

Some titles may be available electronically through the University library.

  • Telea, Alexandru Data visualization : principles and practice


    Find in the library


  • Ward, Matthew; Grinstein, Georges; Keim, Daniel. Interactive data visualization : foundations, techniques, and applications

    Second edition.: Boca Raton: CRC Press, Taylor & Francis Group, [2015]

    Find in the library

  • Wilke, Claus O. Fundamentals of data visualization : a primer on making informative and compelling figures

    First edition.: Sebastopol, CA: O'Reilly Media, 2019

    Find in the library

  • Munzner, Tamara Visualization analysis and design

    Boca Raton: CRC Press, xop. 2015.

    Find in the library

Last modified: 2022-04-26