040309 VU Doing Data Science (MA) (2023S)
Continuous assessment of course work
Labels
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 served).
- Registration is open from Mo 13.02.2023 09:00 to We 22.02.2023 12:00
- Registration is open from Mo 27.02.2023 09:00 to Tu 28.02.2023 12:00
- Deregistration possible until Fr 17.03.2023 23:59
Details
max. 80 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Thursday 02.03. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
- Tuesday 07.03. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
- Thursday 09.03. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
- Tuesday 14.03. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
- Thursday 16.03. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
- Tuesday 21.03. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
- Thursday 23.03. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
- Tuesday 28.03. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
- Thursday 30.03. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
- Tuesday 18.04. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
- Thursday 20.04. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
- Tuesday 25.04. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
- Thursday 27.04. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
- Tuesday 02.05. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
- Thursday 04.05. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
- Tuesday 09.05. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
- Thursday 11.05. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
- Tuesday 16.05. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
- Tuesday 23.05. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
- Thursday 25.05. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
- Thursday 01.06. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
- Tuesday 06.06. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
- Tuesday 13.06. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
- Thursday 15.06. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
- Tuesday 20.06. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
- Thursday 22.06. 13:15 - 16:30 Seminarraum 5, Kolingasse 14-16, EG00
- Tuesday 27.06. 13:15 - 14:45 Seminarraum 5, Kolingasse 14-16, EG00
- Thursday 29.06. 13:15 - 16:30 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%): Do, Mar 30, 15:00
Final test (30%): Do, May 25, 15:00
Project work (40%):
- Review meetings: Do, May 4, 15:00
- Final presentations: Do, Jun 22, 13:15
Final test (30%): Do, May 25, 15:00
Project work (40%):
- Review meetings: Do, May 4, 15:00
- Final presentations: Do, Jun 22, 13:15
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
[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.
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: Mo 27.03.2023 11:28