040172 VU Doing Data Science (MA) (2023W)

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

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 11.09.2023 09:00 to Fr 22.09.2023 12:00
Registration is open from Tu 26.09.2023 09:00 to We 27.09.2023 12:00
Deregistration possible until Fr 20.10.2023 23:59

Details

max. 80 participants

Language: English

Lecturers

Classes (iCal) - next class is marked with N

    
    Tuesday
    03.10.
    13:15 - 16:30
    Seminarraum 5, Kolingasse 14-16, EG00
    
    Wednesday
    04.10.
    15:00 - 16:30
    Seminarraum 5, Kolingasse 14-16, EG00
    
    Tuesday
    10.10.
    13:15 - 16:30
    Seminarraum 5, Kolingasse 14-16, EG00
    
    Wednesday
    11.10.
    15:00 - 16:30
    Seminarraum 5, Kolingasse 14-16, EG00
    
    Tuesday
    17.10.
    13:15 - 16:30
    Seminarraum 5, Kolingasse 14-16, EG00
    
    Wednesday
    18.10.
    15:00 - 16:30
    Seminarraum 5, Kolingasse 14-16, EG00
    
    Tuesday
    24.10.
    13:15 - 16:30
    Seminarraum 5, Kolingasse 14-16, EG00
    
    Wednesday
    25.10.
    15:00 - 16:30
    Seminarraum 5, Kolingasse 14-16, EG00
    
    Tuesday
    31.10.
    13:15 - 16:30
    Seminarraum 5, Kolingasse 14-16, EG00
    
    Tuesday
    07.11.
    13:15 - 16:30
    Seminarraum 5, Kolingasse 14-16, EG00
    
    Wednesday
    08.11.
    15:00 - 16:30
    Hörsaal 6  Oskar-Morgenstern-Platz 1  1.Stock
Seminarraum 5, Kolingasse 14-16, EG00
    
    Tuesday
    14.11.
    13:15 - 16:30
    Seminarraum 5, Kolingasse 14-16, EG00
    
    Wednesday
    15.11.
    15:00 - 16:30
    Seminarraum 5, Kolingasse 14-16, EG00
    
    Tuesday
    21.11.
    13:15 - 16:30
    Seminarraum 5, Kolingasse 14-16, EG00
    
    Wednesday
    22.11.
    15:00 - 16:30
    Seminarraum 5, Kolingasse 14-16, EG00
    
    Tuesday
    28.11.
    13:15 - 16:30
    Seminarraum 5, Kolingasse 14-16, EG00
    
    Wednesday
    29.11.
    15:00 - 16:30
    Seminarraum 5, Kolingasse 14-16, EG00
    
    Tuesday
    05.12.
    13:15 - 16:30
    Seminarraum 5, Kolingasse 14-16, EG00
    
    Wednesday
    06.12.
    15:00 - 16:30
    Seminarraum 5, Kolingasse 14-16, EG00
    
    Tuesday
    12.12.
    13:15 - 16:30
    Seminarraum 5, Kolingasse 14-16, EG00
    
    Wednesday
    13.12.
    15:00 - 16:30
    Seminarraum 5, Kolingasse 14-16, EG00
    
    Tuesday
    09.01.
    13:15 - 16:30
    Seminarraum 5, Kolingasse 14-16, EG00
    
    Wednesday
    10.01.
    15:00 - 16:30
    Seminarraum 5, Kolingasse 14-16, EG00
    
    Tuesday
    16.01.
    13:15 - 16:30
    Seminarraum 5, Kolingasse 14-16, EG00
    
    Wednesday
    17.01.
    15:00 - 16:30
    Seminarraum 5, Kolingasse 14-16, EG00
    
    Tuesday
    23.01.
    13:15 - 16:30
    Seminarraum 5, Kolingasse 14-16, EG00
    
    Wednesday
    24.01.
    15:00 - 16:30
    Seminarraum 5, Kolingasse 14-16, EG00
    
    Tuesday
    30.01.
    13:15 - 16:30
    Seminarraum 5, Kolingasse 14-16, EG00
    
    Wednesday
    31.01.
    15:00 - 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%): Nov 8, 15:00-16:00, allowed is only a calculator.
Final test (30%): Dec 6, 15:00-16:00, allowed is only a calculator.
Project work (40%):
- Review meetings: Dec 12, 15:00-16:30
- Posters and videos due: Jan 21, 23:59
- Final presentations: Jan 23, 13:15-14:45, and Jan 24, 15:00-16:30

The use of AI tools (e.g. ChatGPT) for the handling of tasks is only permitted if they are expressly requested by the course leader (e.g. for individual work tasks).

Minimum requirements and assessment criteria

For midterm and final test as well as project work, attendance is mandatory, including kick-off 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: We 24.01.2024 12:25