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040123 KU Programming for Business Analytics (MA) (2022W)
Continuous assessment of course work
Labels
REMOTE
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 12.09.2022 09:00 to Fr 23.09.2022 12:00
- Registration is open from We 28.09.2022 09:00 to Th 29.09.2022 12:00
- Deregistration possible until Fr 14.10.2022 23:59
Details
max. 50 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Wednesday 05.10. 11:30 - 13:00 Digital
- Wednesday 12.10. 11:30 - 13:00 Digital
- Wednesday 19.10. 11:30 - 13:00 Digital
- Wednesday 09.11. 11:30 - 13:00 Digital
- Wednesday 16.11. 11:30 - 13:00 Digital
- Wednesday 23.11. 11:30 - 13:00 Digital
- Wednesday 30.11. 11:30 - 13:00 Digital
- Wednesday 07.12. 11:30 - 13:00 Digital
- Wednesday 14.12. 11:30 - 13:00 Digital
- Wednesday 11.01. 11:30 - 13:00 Digital
- Wednesday 18.01. 11:30 - 13:00 Digital
- Wednesday 25.01. 11:30 - 13:00 Digital
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 content is provided in the form of video files. A continuous learning is assured by weekly homework assignments.
Assessment and permitted materials
Homework (20%), Midterm exam (30%), Endterm exam (40%), Theory exam (10%).
All submission will be online via Moodle. The exams will be online as well.
All submission will be online via Moodle. The exams will be online as well.
Minimum requirements and assessment criteria
For a positive grade, students have to achieve at least 50 percent (overall score).
Grading key:
4: 50% to <63%
3: 63% to <75%
2: 75% to <90%
1: 90% to 100%
Grading key:
4: 50% to <63%
3: 63% to <75%
2: 75% to <90%
1: 90% to 100%
Examination topics
Lecture notes, literature excerpts, home assignments
Reading list
Deitel, P. J., & Dietal, H. (2020). Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud. Pearson Education, Incorporated.
Association in the course directory
Last modified: Th 11.05.2023 11:27