Syllabus for Scientific Visualisation
- 7.5 credits
- Course code: 1MD140
- Education cycle: Second cycle
Main field(s) of study and in-depth level:
Computer Science A1N,
Image Analysis and Machine Learning A1N,
Computational Science A1N
- Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Established: 2021-03-04
- Established by:
- Revised: 2022-02-04
- Revised by: The Faculty Board of Science and Technology
- Applies from: Autumn 2022
120 credits including a basic course in programming. Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Responsible department: Department of Information Technology
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.
The course includes projects using software for advanced visualisations.
Lectures, computer exercises and project work.
Written test (3 credits). Assignments (2.5 credits). Project (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.
The course cannot be included in the same degree as 1TD389 Scientific Visualisation.
Applies from: Autumn 2022
Some titles may be available electronically through the University library.
Data visualization : principles and practice
Interactive data visualization : foundations, techniques, and applications
Second edition.: Boca Raton: CRC Press, Taylor & Francis Group, 
Wilke, Claus O.
Fundamentals of data visualization : a primer on making informative and compelling figures
First edition.: Sebastopol, CA: O'Reilly Media, 2019
Visualization analysis and design
Boca Raton: CRC Press, xop. 2015.