Universität Wien
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040172 VU Doing Data Science (MA) (2020W)

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

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

Watch the short welcome video!

This is a hybrid class: generally, lectures will be recorded. This means that you can participate in the classroom (with distance) or online and/or watch the recorded lecture at a later time.

Lecture times are Tuesday, 15:00-16:30, and Wednesday, 13:15-14:45. Other times and rooms are reserved for lab work and will be announced separately. We encourage you to participate in lectures during these times, since this allows for personal interaction (in the classroom or via online communication).

The first appointment will be Tuesday, Oct 6, 15:00

  • Tuesday 06.10. 15:00 - 16:30 Digital
  • Wednesday 07.10. 13:15 - 14:45 Digital
  • Wednesday 07.10. 15:00 - 16:30 Digital
  • Tuesday 13.10. 13:15 - 14:45 Digital
  • Tuesday 13.10. 15:00 - 16:30 Digital
  • Wednesday 14.10. 13:15 - 14:45 Digital
  • Wednesday 14.10. 15:00 - 16:30 Digital
  • Tuesday 20.10. 13:15 - 14:45 Digital
  • Tuesday 20.10. 15:00 - 16:30 Digital
  • Wednesday 21.10. 13:15 - 14:45 Digital
  • Wednesday 21.10. 15:00 - 16:30 Digital
  • Tuesday 27.10. 13:15 - 14:45 Digital
  • Tuesday 27.10. 15:00 - 16:30 Digital
  • Wednesday 28.10. 13:15 - 14:45 Digital
  • Wednesday 28.10. 15:00 - 16:30 Digital
  • Tuesday 03.11. 13:15 - 14:45 Digital
  • Tuesday 03.11. 15:00 - 16:30 Digital
  • Wednesday 04.11. 13:15 - 14:45 Digital
  • Wednesday 04.11. 15:00 - 16:30 Digital
  • Tuesday 10.11. 13:15 - 14:45 Digital
  • Tuesday 10.11. 15:00 - 16:30 Digital
  • Wednesday 11.11. 13:15 - 14:45 Digital
  • Wednesday 11.11. 15:00 - 16:30 Digital
  • Tuesday 17.11. 13:15 - 14:45 Digital
  • Wednesday 18.11. 15:00 - 16:30 Digital
  • Tuesday 24.11. 13:15 - 14:45 Digital
  • Tuesday 24.11. 15:00 - 16:30 Digital
  • Wednesday 25.11. 13:15 - 14:45 Digital
  • Wednesday 25.11. 15:00 - 16:30 Digital
  • Tuesday 01.12. 13:15 - 14:45 Digital
  • Tuesday 01.12. 15:00 - 16:30 Digital
  • Wednesday 02.12. 13:15 - 14:45 Digital
  • Wednesday 02.12. 15:00 - 16:30 Digital
  • Tuesday 08.12. 15:00 - 16:30 Digital
  • Wednesday 09.12. 13:15 - 14:45 Digital
  • Wednesday 09.12. 15:00 - 16:30 Digital
  • Tuesday 15.12. 13:15 - 14:45 Digital
  • Tuesday 15.12. 15:00 - 16:30 Digital
  • Wednesday 16.12. 13:15 - 14:45 Digital
  • Wednesday 16.12. 15:00 - 16:30 Digital
  • Tuesday 12.01. 13:15 - 14:45 Digital
  • Tuesday 12.01. 15:00 - 16:30 Digital
  • Tuesday 19.01. 13:15 - 14:45 Digital
  • Tuesday 19.01. 15:00 - 16:30 Digital
  • Wednesday 20.01. 13:15 - 14:45 Digital
  • Wednesday 20.01. 15:00 - 16:30 Digital
  • Tuesday 26.01. 13:15 - 14:45 Digital
  • Wednesday 27.01. 15:00 - 16:30 Digital

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 11, 15:00
Final test (30%): Dez 15, 15:00
Project work (40%): Ongoing, final presentations: Jan 26, Jan 27

Minimum requirements and assessment criteria

Midterm test and one more examination (project work / final test) must be passed individually. **
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

Provost/Fawcett: Data Science for Business - What you need to know about data mining and data-analytic thinking. http://www.data-science-for-biz.com/

Silipo: KNIME Beginner's Luck - A Guide to KNIME Analytics Platform for beginners. https://www.knime.com/knimepress/beginners-luck

Association in the course directory

Last modified: Fr 12.05.2023 00:12