040123 KU Programming for Business Analytics (MA) (2021W)
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 13.09.2021 09:00 to Th 23.09.2021 12:00
- Registration is open from Mo 27.09.2021 09:00 to We 29.09.2021 12:00
- Deregistration possible until Fr 15.10.2021 23:59
Details
max. 50 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Thursday 07.10. 09:45 - 11:15 Digital
- Thursday 14.10. 09:45 - 11:15 Digital
- Thursday 21.10. 09:45 - 11:15 Digital
- Thursday 28.10. 09:45 - 11:15 Digital
- Thursday 04.11. 09:45 - 11:15 Digital
- Thursday 11.11. 09:45 - 11:15 Digital
- Thursday 18.11. 09:45 - 11:15 Digital
- Thursday 25.11. 09:45 - 11:15 Digital
- Thursday 02.12. 09:45 - 11:15 Digital
- Thursday 09.12. 09:45 - 11:15 Digital
- Thursday 16.12. 09:45 - 11:15 Digital
- Thursday 13.01. 09:45 - 11:15 Digital
- Thursday 20.01. 09:45 - 11:15 Digital
- Thursday 27.01. 09:45 - 11:15 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: Fr 12.05.2023 00:12