186.833 Visualization 2
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2023W, VU, 3.0h, 4.5EC


  • Semester hours: 3.0
  • Credits: 4.5
  • Type: VU Lecture and Exercise
  • Format: Hybrid

Learning outcomes

After successful completion of the course, students are able to understand advanced theoretical concepts of visualization. Furthermore, students are able to implement programming tasks resulting from a scientific article.

Subject of course

The VU Visualization 2 consists of a lecture part and an exercise part.

The lecture part consists of a series of lectures which take place throughout the semester. Lectures take place Thursdays 3:00 p.m. (c.t.). In the lectures advanced special topics are treated, such as visualization of text data or spatio-temporal data, immersive analytics, visualization of large networks, or the role of generative networks in data visualization and analysis. 

The exercise part consists of the implementation of a state-of-the-art visualization technique based on a scientific article. The exercise is ideally implemented in groups of two. Choose from a list of given scientific articles in TUWEL or propose your own paper. There are no requirements regarding programming languages. The lab is supported by a tutor. 

The grade for Visualization 2 will be based on the combination of the practical work and the theoretical knowledge. The exact score for the grading results as follows:

  • a short proposal about the implementation idea (5 points)
  • the 1st presentation of the article incl. implementation idea (5 points)
  • the implementation and documentation (40 points)
  • the 2nd presentation of the program (10 points)
  • the oral lecture exam (40 points)

For a positive grade, both lecture and exercise must be completed positively!

The schedule, zoom links, recordings, lecture notes, exercise notes and handouts, and exercise forums are located in TUWEL.

See also: Vis2 Hall of Fame

Teaching methods

Lectures with slides on advanced areas of visualization. Discussion groups. Programming exercises.

Mode of examination


Additional information

ATTENTION: Since 2023W, this course is offered exclusively in winter semester! 

ECTS breakdown: 4,5 ECTS = 112,5 hours

25 hours - presentation and documentation of article and implementation idea
50 hours - implementation of practical example

37,5 hours - lecture attendance and preparation for oral exam




Course dates

Thu15:00 - 17:0005.10.2023 - 25.01.2024Seminarraum FAV 05 (Seminarraum 186) (LIVE)Lecture
Visualization 2 - Single appointments
Thu05.10.202315:00 - 17:00Seminarraum FAV 05 (Seminarraum 186) Lecture
Thu12.10.202315:00 - 17:00Seminarraum FAV 05 (Seminarraum 186) Lecture
Thu19.10.202315:00 - 17:00Seminarraum FAV 05 (Seminarraum 186) Lecture
Thu09.11.202315:00 - 17:00Seminarraum FAV 05 (Seminarraum 186) Lecture
Thu16.11.202315:00 - 17:00Seminarraum FAV 05 (Seminarraum 186) Lecture
Thu23.11.202315:00 - 17:00Seminarraum FAV 05 (Seminarraum 186) Lecture
Thu30.11.202315:00 - 17:00Seminarraum FAV 05 (Seminarraum 186) Lecture
Thu07.12.202315:00 - 17:00Seminarraum FAV 05 (Seminarraum 186) Lecture
Thu14.12.202315:00 - 17:00Seminarraum FAV 05 (Seminarraum 186) Lecture
Thu21.12.202315:00 - 17:00Seminarraum FAV 05 (Seminarraum 186) Lecture
Thu11.01.202415:00 - 17:00Seminarraum FAV 05 (Seminarraum 186) Lecture
Thu18.01.202415:00 - 17:00Seminarraum FAV 05 (Seminarraum 186) Lecture
Thu25.01.202415:00 - 17:00Seminarraum FAV 05 (Seminarraum 186) Lecture

Examination modalities

  • Lecture exam on the theoretical concepts.
  • Submission talk regarding the programming task.
  • Presentation of the selected article, as well as proposal for the planned implementation. 
  • Presentation of the program. 

All dates are hybrid (seminar room + Zoom) or remote (Zoom); for remote participation, audio- and video connection is necessary. 

Course registration

Begin End Deregistration end
01.09.2023 00:00


Study CodeObligationSemesterPrecon.Info
066 645 Data Science Not specified
066 926 Business Informatics Mandatory elective
066 932 Visual Computing Mandatory3. Semester


No lecture notes are available.

Previous knowledge

Programming experience and basic knowledge of visualization is highly recommended.