Universität Wien

040309 VU Doing Data Science (MA) (2026S)

6.00 ECTS (4.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work

The course language is English.

Only students who signed up for the class in univis/u:space are allowed to take the class (that means, that you have to at least be on the waiting list if you want to take this class). No exceptions possible.
Moodle
Tu 21.04. 13:15-16:30 Seminarraum 5, Kolingasse 14-16, EG00

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 80 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

The first lecture will be on March 5, 2026. Please also consider the schedule in Moodle.

  • Thursday 05.03. 16:45 - 18:15 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 10.03. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 17.03. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Thursday 19.03. 16:45 - 18:15 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 24.03. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Thursday 26.03. 16:45 - 18:15 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 14.04. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Thursday 16.04. 16:45 - 18:15 Seminarraum 5, Kolingasse 14-16, EG00
  • Thursday 23.04. 16:45 - 18:15 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 28.04. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Thursday 30.04. 16:45 - 18:15 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 05.05. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Thursday 07.05. 16:45 - 18:15 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 12.05. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 19.05. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Thursday 21.05. 16:45 - 18:15 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 26.05. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Thursday 28.05. 16:45 - 18:15 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 02.06. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 09.06. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Thursday 11.06. 16:45 - 18:15 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 16.06. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Thursday 18.06. 16:45 - 18:15 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 23.06. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Thursday 25.06. 16:45 - 18:15 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 30.06. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00

Information

Aims, contents and method of the course

This course covers the fundamentals of setting up, managing, and conducting data science projects. Students acquire knowledge of processes describing how to approach and implement data science projects. They know the particular steps of the CRISP industry-standard, learn about various cases of how to apply this to different applications (from different areas such as business, humanities, astronomy), and are able to conduct data science projects themselves.

This course consists of lectures, tutorials, showcases, and project presentations. Students will work on their own data science projects in interdisciplinary groups.

Assessment and permitted materials

Midterm test (40%): Apr 21, 13:15-14:15, SR5
Final test (30%): May 26, 13:15-14:15, SR5
Project work (30%):
- Review meetings: June 04, 16:45-18:15
- Final presentations: June 30, 13:15-16:30, SR5

The midterm test has to be passed to be admitted to the project work.

1) The course is passed when 50% of the total points have been reached.
2) The use of AI tools (e.g. ChatGPT) for the production of texts is only allowed, if this is expressly requested by the course instructor (e.g. for specific assignments).
3) To ensure good scientific practice, the course instructor may request a "grade-relevant talk" (plausibility check) regarding the submitted written work. This interview has to be completed successfully.

Minimum requirements and assessment criteria

For project work, attendance is mandatory, including kick-off, review meetings, and project presentations.

In total, 100 points can be achieved. Grades are assigned as follows:
[88,100]: 1
[76,88[ : 2
[63,76[ : 3
[50,63[ : 4
< 50 : 5

Examination topics

Midterm test/Final test: Slides and topics covered in the lectures.
Project work: topic-specific poster presentation, handout, KNIME workflow.

Reading list

Provost, Foster; Fawcett, Tom (2013): Data Science for Business. What you need to know about data mining and data-analytic thinking. Köln: O`Reilly.

Berthold, Michael R.; Borgelt, Christian; Höppner, Frank; Klawonn, Frank; Silipo, Rosaria (2020): Guide to Intelligent Data Science. Cham: Springer International Publishing.

Association in the course directory

Last modified: Tu 24.02.2026 10:06