Universität Wien

040309 VU Doing Data Science (MA) (2022S)

6.00 ECTS (2.00 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. 40 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

This class will be offered in hybrid form. There will be lecture videos that students are expected to watch and discuss. All live appointments will be streamed through Moodle/Zoom. First meeting: Tuesday, March 1st, 13:15-14:45, SR1, Kolingasse 14-16.

  • Tuesday 01.03. 13:15 - 14:45 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 02.03. 15:00 - 16:30 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 08.03. 13:15 - 14:45 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 09.03. 15:00 - 16:30 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 15.03. 13:15 - 14:45 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 16.03. 15:00 - 16:30 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 22.03. 13:15 - 14:45 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 23.03. 15:00 - 16:30 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 29.03. 13:15 - 14:45 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 30.03. 15:00 - 16:30 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 05.04. 13:15 - 14:45 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 06.04. 15:00 - 16:30 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 26.04. 13:15 - 14:45 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 27.04. 15:00 - 16:30 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 03.05. 13:15 - 14:45 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 04.05. 15:00 - 16:30 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 10.05. 13:15 - 14:45 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 11.05. 15:00 - 16:30 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 17.05. 13:15 - 14:45 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 18.05. 15:00 - 16:30 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 24.05. 13:15 - 14:45 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 25.05. 15:00 - 16:30 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 31.05. 13:15 - 14:45 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 01.06. 15:00 - 16:30 Digital
    Hybride Lehre
  • Wednesday 08.06. 15:00 - 16:30 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 14.06. 13:15 - 14:45 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 15.06. 15:00 - 16:30 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 21.06. 13:15 - 14:45 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 22.06. 15:00 - 16:30 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Tuesday 28.06. 13:15 - 14:45 Hybride Lehre
    PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Wednesday 29.06. 15:00 - 16:30 Hybride Lehre
    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%): March 30, 15:00
Final test (30%): May 17, 13:15
Project work (40%):
- Review meetings: May 24, 15:00
- Final presentations: **June 22, 15:00**

Minimum requirements and assessment criteria

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

In total, 100 points can be achieved. Grades are assigned as follows:
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 11.05.2023 11:27