040123 KU Programming for Business Analytics (MA) (2021W)
Prüfungsimmanente Lehrveranstaltung
Labels
DIGITAL
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.
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 13.09.2021 09:00 bis Do 23.09.2021 12:00
- Anmeldung von Mo 27.09.2021 09:00 bis Mi 29.09.2021 12:00
- Abmeldung bis Fr 15.10.2021 23:59
Details
max. 50 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
- Donnerstag 07.10. 09:45 - 11:15 Digital
- Donnerstag 14.10. 09:45 - 11:15 Digital
- Donnerstag 21.10. 09:45 - 11:15 Digital
- Donnerstag 28.10. 09:45 - 11:15 Digital
- Donnerstag 04.11. 09:45 - 11:15 Digital
- Donnerstag 11.11. 09:45 - 11:15 Digital
- Donnerstag 18.11. 09:45 - 11:15 Digital
- Donnerstag 25.11. 09:45 - 11:15 Digital
- Donnerstag 02.12. 09:45 - 11:15 Digital
- Donnerstag 09.12. 09:45 - 11:15 Digital
- Donnerstag 16.12. 09:45 - 11:15 Digital
- Donnerstag 13.01. 09:45 - 11:15 Digital
- Donnerstag 20.01. 09:45 - 11:15 Digital
- Donnerstag 27.01. 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.
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 <90%
1: 90% to 100%
Grading key:
4: 50% to <63%
3: 63% to <75%
2: 75% to <90%
1: 90% 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