Universität Wien FIND

Due to the COVID-19 pandemic, changes to courses and exams may be necessary at short notice (e.g. cancellation of on-site teaching and conversion to online exams). Register for courses/exams via u:space, find out about the current status on u:find and on the moodle learning platform. NOTE: Courses where at least one unit is on-site are currently marked "on-site" in u:find.

Further information about on-site teaching and access tests can be found at https://studieren.univie.ac.at/en/info.

Warning! The directory is not yet complete and will be amended until the beginning of the term.

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.

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first serve).

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: We 25.11.2020 14:07