040123 KU Programming for Business Analytics (MA) (2023W)
Continuous assessment of course work
Labels
MIXED
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 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. 50 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Thursday 05.10. 15:00 - 18:15 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
- Thursday 12.10. 15:00 - 18:15 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
- Tuesday 17.10. 16:45 - 18:15 Digital
- Thursday 19.10. 15:00 - 18:15 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
- Tuesday 31.10. 16:45 - 18:15 Digital
- Thursday 09.11. 15:00 - 18:15 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
- Tuesday 14.11. 16:45 - 18:15 Digital
- Thursday 16.11. 15:00 - 18:15 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
- Thursday 23.11. 15:00 - 18:15 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
- Tuesday 28.11. 16:45 - 18:15 Digital
- Thursday 30.11. 15:00 - 18:15 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
- Thursday 07.12. 15:00 - 18:15 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
- Tuesday 12.12. 16:45 - 18:15 Digital
- Thursday 14.12. 15:00 - 18:15 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
- Tuesday 09.01. 16:45 - 18:15 Digital
- Thursday 11.01. 15:00 - 18:15 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
- Thursday 18.01. 15:00 - 18:15 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
- Tuesday 23.01. 16:45 - 18:15 Digital
- Thursday 25.01. 15:00 - 18:15 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
Information
Aims, contents and method of the course
The main scope of this course is an introduction to the programming language Python. The course covers the basics of programming, as well as in depth skills necessary for data analysis and optimization algorithms. The course consists of a weekly on-site lecture (3 hours) and a biweekly online exercise session (2 hours).
Assessment and permitted materials
Exercises 30% - Submitted to Moodle before exercise session.
Theoretical exam 30% - On site exam at the end of the term.
Applied project 40% - Final discussions will take place in February and March.
Theoretical exam 30% - On site exam at the end of the term.
Applied project 40% - Final discussions will take place in February and March.
Minimum requirements and assessment criteria
x = Total points in percent5: x < 50%
4: 50% <= x < 62.5%
3: 62.5% <= x < 75%
2: 75% <= x < 87.5%
1: 87.5% <= x
4: 50% <= x < 62.5%
3: 62.5% <= x < 75%
2: 75% <= x < 87.5%
1: 87.5% <= x
Examination topics
Lecture notes, literature excerpts, home assignments
Reading list
An Introduction to Statistical Learning with Applications in Python (2023) by G. James, D. Witten, T. Hastie, R. Tibshirani and J. TaylorAvailable for free download: https://www.statlearning.com
Association in the course directory
Last modified: Tu 26.09.2023 06:46