Universität Wien

040172 VU Doing Data Science (MA) (2024W)

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 08.10. 13:15-14:45 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).
Register/Deregister for this course

Details

max. 80 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Participation is prioritized for students of Business Analytics, Data Science and Digital Humanities.
The first lecture will be on Tuesday, October 8th.

  • Tuesday 08.10. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Wednesday 09.10. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 15.10. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 15.10. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Wednesday 16.10. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 22.10. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 22.10. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Wednesday 23.10. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 29.10. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 29.10. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Wednesday 30.10. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 05.11. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 05.11. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Wednesday 06.11. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 12.11. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 12.11. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 19.11. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 19.11. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Wednesday 20.11. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 26.11. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 26.11. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Wednesday 27.11. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 03.12. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 03.12. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Wednesday 04.12. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 10.12. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 10.12. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Wednesday 11.12. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 17.12. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 17.12. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 07.01. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 07.01. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Wednesday 08.01. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 14.01. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 14.01. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Wednesday 15.01. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 21.01. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 21.01. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Wednesday 22.01. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 28.01. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
  • Tuesday 28.01. 15:00 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
  • Wednesday 29.01. 15:00 - 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. When the theoretical part has been finished, students apply data science methods, create a poster, and present their results.

Assessment and permitted materials

Midterm test (30%): Tue, Nov 12, 13:15, allowed is only a calculator.
Final test (30%): Wed, Dec 11, 15:00, allowed is only a calculator.
Project work (40%):
- Review meetings: Tue, Dec 12, 15:00
- Posters and videos due: Sun, Jan 26, 23:59
- Final presentations: Tue, Jan 28, 13:15-16:30

1) The course is only passed when at least 50% of the total points have been achieved.
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 midterm and final test as well as project work, attendance is mandatory, including kick-off 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: Th 03.10.2024 16:45