Universität Wien
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040172 VU Doing Data Science (MA) (2021W)

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

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

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

This class will be offered in hybrid form. As long as allowed, we will offer one on-site appointment per week (see schedule). To attend, proof of 3G (vaccinated/tested/recovered) and registration will be necessary. All appointments (online and on-site) will be streamed through Moodle/BigBlueButton.

  • Tuesday 05.10. 15:00 - 16:30 Digital
  • Tuesday 12.10. 15:00 - 16:30 Digital
  • Wednesday 13.10. 13:15 - 14:45 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 13.10. 15:00 - 16:30 Digital
  • Tuesday 19.10. 15:00 - 16:30 Digital
  • Wednesday 20.10. 13:15 - 14:45 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 20.10. 15:00 - 16:30 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 27.10. 13:15 - 14:45 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 27.10. 15:00 - 16:30 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 03.11. 13:15 - 14:45 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 03.11. 15:00 - 16:30 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 09.11. 15:00 - 16:30 Digital
  • Wednesday 10.11. 13:15 - 14:45 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 10.11. 15:00 - 16:30 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 16.11. 15:00 - 16:30 Digital
  • Wednesday 17.11. 13:15 - 14:45 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 17.11. 15:00 - 16:30 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 23.11. 15:00 - 16:30 Digital
  • Wednesday 24.11. 13:15 - 14:45 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 24.11. 15:00 - 16:30 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 30.11. 15:00 - 16:30 Digital
  • Wednesday 01.12. 13:15 - 14:45 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 01.12. 15:00 - 16:30 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 07.12. 15:00 - 16:30 Digital
  • Tuesday 14.12. 15:00 - 16:30 Digital
  • Wednesday 15.12. 13:15 - 14:45 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 15.12. 15:00 - 16:30 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 11.01. 15:00 - 16:30 Digital
  • Wednesday 12.01. 13:15 - 14:45 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 12.01. 15:00 - 16:30 Digital
  • Tuesday 18.01. 15:00 - 16:30 Digital
  • Wednesday 19.01. 13:15 - 14:45 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 19.01. 15:00 - 16:30 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 25.01. 15:00 - 16:30 Digital
  • Wednesday 26.01. 13:15 - 14:45 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 26.01. 15:00 - 16:30 Digital
    PC-Seminarraum 1, Kolingasse 14-16, OG01

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 (30%): Nov 16, 15:00
Final test (30%): Dez 14, 15:00
Project work (40%): Ongoing, final presentations: Jan 18, Jan 19

Minimum requirements and assessment criteria

**UPDATED 2021-11-21** Two examinations must be passed individually (e.g. midterm test plus project work or final test plus project work).**
For project work, attendance is mandatory, including kick-off and project presentations.

In total, 100 points can be achieved. Grades are assigned as follows:
1 (very good) • 100-90 points
2 (good) • 89-76 points
3 (satisfactory) • 75-63 points
4 (sufficient) • 62-50 points
5 (not enough) • 49-0 points

Examination topics

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

Reading list

See lecture

Association in the course directory

Last modified: Fr 12.05.2023 00:12