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030598 KU Legal Data Science (2021W)
Continuous assessment of course work
Labels
REMOTE
Teilnahmevoraussetzung: Anfängerkenntnisse Programmieren
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 13.09.2021 00:01 to Mo 27.09.2021 23:59
- Deregistration possible until Th 14.10.2021 23:59
Details
max. 25 participants
Language: German
Lecturers
Classes (iCal) - next class is marked with N
- Wednesday 13.10. 14:30 - 16:00 Digital
- Wednesday 20.10. 14:30 - 16:00 Digital
- Wednesday 27.10. 14:30 - 16:00 Digital
- Wednesday 03.11. 14:30 - 16:00 Digital
- Wednesday 10.11. 14:30 - 16:00 Digital
- Wednesday 17.11. 14:30 - 16:00 Digital
- Wednesday 24.11. 14:30 - 16:00 Digital
- Wednesday 01.12. 14:30 - 16:00 Digital
- Wednesday 15.12. 14:30 - 16:00 Digital
- Wednesday 12.01. 14:30 - 16:00 Digital
- Wednesday 19.01. 14:30 - 16:00 Digital
Information
Aims, contents and method of the course
Assessment and permitted materials
Preparation for classes, group project, presentation, participation in class
Minimum requirements and assessment criteria
Basic programming knowledge (python) required.
This prerequisite is fulfilled if the course KU 030258 "Coding for lawyers" has been completed or is taken in parallel. Programming skills acquired in other ways, such as (university or school) courses, online tutorials or self taught experience are also sufficient.
This prerequisite is fulfilled if the course KU 030258 "Coding for lawyers" has been completed or is taken in parallel. Programming skills acquired in other ways, such as (university or school) courses, online tutorials or self taught experience are also sufficient.
Examination topics
Group project
Reading list
Freely available online material will be provided on Moodle.
Association in the course directory
Last modified: Fr 12.05.2023 00:12
Students will learn theoretical skills such as acquiring data, extracting information, handling and analysis of data. This knowledge will then be applied through practical group projects.