Universität Wien

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

8.00 ECTS (4.00 SWS), SPL 4 - Wirtschaftswissenschaften
Prüfungsimmanente Lehrveranstaltung

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

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 50 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

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.

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

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

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.

Art der Leistungskontrolle und erlaubte Hilfsmittel

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.

Mindestanforderungen und Beurteilungsmaßstab

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%

Prüfungsstoff

Lecture notes, literature excerpts, home assignments

Literatur

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.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Fr 12.05.2023 00:12