Universität Wien

040123 KU Programming for Business Analytics (MA) (2020W)

8.00 ECTS (4.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work

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).

Details

max. 50 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Please note that (online) attendance is NOT compulsory, EXCEPT for the last two sessions (January 21 and 28).
Question times will take place as of November 5 (live, online).
There will be no live sessions on October 8, 15, 22, and 29.

  • Thursday 01.10. 09:45 - 11:15 Digital
  • Thursday 08.10. 09:45 - 11:15 Digital
  • Thursday 15.10. 09:45 - 11:15 Digital
  • Thursday 22.10. 09:45 - 11:15 Digital
  • Thursday 29.10. 09:45 - 11:15 Digital
  • Thursday 05.11. 09:45 - 11:15 Digital
  • Thursday 12.11. 09:45 - 11:15 Digital
  • Thursday 19.11. 09:45 - 11:15 Digital
  • Thursday 26.11. 09:45 - 11:15 Digital
  • Thursday 03.12. 09:45 - 11:15 Digital
  • Thursday 10.12. 09:45 - 11:15 Digital
  • Thursday 17.12. 09:45 - 11:15 Digital
  • Thursday 07.01. 09:45 - 11:15 Digital
  • Thursday 14.01. 09:45 - 11:15 Digital
  • Thursday 21.01. 09:45 - 11:15 Digital
  • Thursday 28.01. 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.

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 <87%
1: 87% 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