Computer Engineering MA, Visualization, 6 credits


Computer Engineering MA, Visualization, 6 credits

General data

  • Code: DT056A
  • Subject/Main field: Computer Engineering
  • Cycle: Second cycle
  • Credits: 6
  • Progressive specialization: A1N - Second cycle, has only first-cycle course/s as entry requirements
  • Answerable department: Information Systems and Technology
  • Approved: 6/20/2018
  • Date of change: 12/1/2020
  • Version valid from: 1/1/2020


The course aims towards a good understanding of visualization principles and algorithms. The course also gives insight into visualization techniques and tools as support to end users in their analytical reasoning. Increasingly complex data types including scalars, vectors, images, volumes and non-spatial datasets are explored with respect to their proper visualization techniques and algorithms. Issues such as data representation (data encoding), presentation (layout) and interaction (with user) are discussed. Visualization software systems and libraries are central to creating successful visualizations and are also introduced.

Course objectives

After finishing this course the student should be able to
- describe basic concepts in visualization
- describe desirable properties of a good visualization mapping
- apply required steps towards a good visualization given a defined problem
- use a visualization system or frame work for visualization of scalar data, vector data and volume data including time dependent data
- apply methods for visualization related to a specific problem using a visualization frame work or as plugins for a visualization system
- evaluate the performance of a visualization design using relevant quality metrics
- analyze a practical problem using a visualization system or frame work.


- Overview of data and information visualization
- Visualization pipeline
- Data representation
- Scalar, vector, image, and volume algorithms
- Information visualization
- Interactive visualization
- Visualization systems, -frame work, and -APIs

Entry requirements

Bachelors Degree in Computer Science or Computer Engineering, including courses in programming in C++, 15 credits. Mathematics, Linear Algebra, 7.5 credits; Probability Theory, 7.5 credits.

Selection rules and procedures

The selection process is in accordance with the Higher Education Ordinance and the local order of admission.

Teaching form

The course is taught using lectures, laboratory sessions, and finally a written exam. The large part of the course is with limited supervision, where the student is assumed to work on lecture material, and laboratory work.

Examination form

L101: Laboratory work, 1.5 Credits
Grade scale: Fail (U) or Pass (G)

T101: Exam, 4.5 Credits
Grade scale: Seven-grade scale, A, B, C, D, E, Fx and F. Fx and F represent fail levels.

Grading criteria for the subject can be found at

The examiner has the right to offer alternative examination arrangements to students who have been granted the right to special support by Mid Sweden University’s disabilities adviser.

If examination on campus cannot be conducted according to decision by the vice-chancellor, or whom he delegated the right to, the following applies: [Written Exam T101], will be replaced with two parts, online examination and follow-up. Within three weeks of the online examination, a selection of students will be contacted and asked questions regarding the examination. The follow-up consists of questions concerning the execution of the on-line exam and the answers that the student have submitted.

Grading system

Seven-grade scale, A, B, C, D, E, Fx and F. Fx and F represent fail levels.

Course reading

Required literature

  • Author: Alexandru C Telea
  • Title: Data visualization: principles and practice
  • Edition: 2014
  • Publisher: CRC Press

The page was updated 9/2/2014