Universität Wien

040309 VU Doing Data Science (MA) (2024S)

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

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 07.05. 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).

Details

max. 80 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Tuesday 05.03. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
Thursday 07.03. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
Thursday 14.03. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
Tuesday 19.03. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
Thursday 21.03. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
Tuesday 09.04. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
Thursday 11.04. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
Tuesday 16.04. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
Thursday 18.04. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
Tuesday 23.04. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
Thursday 25.04. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
Tuesday 30.04. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
Thursday 02.05. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
Tuesday 14.05. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
Thursday 16.05. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
Seminarraum 9, Kolingasse 14-16, OG01
Tuesday 21.05. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
Thursday 23.05. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
Tuesday 28.05. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
Tuesday 04.06. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
Thursday 06.06. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
Tuesday 11.06. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
Thursday 13.06. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
Tuesday 18.06. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
Thursday 20.06. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
Tuesday 25.06. 13:15 - 14:45 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 (30%): Apr 23, 13:15-14:15, SR5
Final test (30%): May 21, 13:15-14:15, SR5
Project work (40%):
- Review meetings: May 28, 13:15-14:45
- Final presentations: June 25, 13:15-14:45, SR5

1) Fulfilling all partial achievements is a prerequisite for positive assessment, if nothing else has been explicitly stated.
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.

For more details, see here: https://ufind.univie.ac.at/en/vvz_sub.html?from=1&to=2&path=S8504&semester=2024S

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 12.03.2024 13:05