Syllabus for Scientific Visualisation

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.

Content

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.

Instruction

Lectures, laboratory work and compulsory assignments.

Assessment

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.