Universität Wien FIND
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040164 KU Python for Finance I (MA) (2020W)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Prüfungsimmanente Lehrveranstaltung


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


max. 35 Teilnehmer*innen
Sprache: Englisch


Termine (iCal) - nächster Termin ist mit N markiert

Montag 05.10. 13:15 - 14:45 Digital
Montag 12.10. 13:15 - 14:45 Digital
Montag 19.10. 13:15 - 14:45 Digital
Montag 09.11. 13:15 - 14:45 Digital
Montag 16.11. 13:15 - 14:45 Digital
Montag 23.11. 13:15 - 14:45 Digital
Montag 30.11. 13:15 - 14:45 Digital
Montag 07.12. 13:15 - 14:45 Digital
Montag 14.12. 13:15 - 14:45 Digital
Montag 11.01. 13:15 - 14:45 Digital
Montag 18.01. 13:15 - 14:45 Digital
Montag 25.01. 13:15 - 14:45 Digital


Ziele, Inhalte und Methode der Lehrveranstaltung

The course provides an introduction to Python, a programming language that has become popular in the financial industry besides other quantitative fields. Participants do not need prior programming experience, though they should have successfully completed Basics of Finance or comparable courses.
We will start with an introduction to programming and the basics of Python. Subsequently, the course will consist of an introduction to some of the Python packages most relevant for applications in Finance.
This course is of an applied nature, with the goal of enabling students to use Python to solve problems they may encounter in practice.

Main Topics of the Course:

1. Python and Programming Basics
2. Numerical Computing with NumPy
3. Data Analysis with pandas

Other topics: data Visualization with matplotlib and regression analysis with statsmodels and linearmodels.

Art der Leistungskontrolle und erlaubte Hilfsmittel

The grade will be based on homework exercises that participants are expected to present in class, active class participation, and a final exam. The final exam will take place on Moodle on January, 25.

The course will be taught via video conferencing.

Mindestanforderungen und Beurteilungsmaßstab

60% homework exercises
10% active class participation
30% final exam

Minimum requirement for a positive grade: a total of 50%.


All material covered in class.


Main reference:

Sheppard, Kevin. Introduction to Python for Econometrics, Statistics and Data Analysis, 2019. https://www.kevinsheppard.com/files/teaching/python/notes/python_introduction_2019.pdf

McKenney, Wes. Python for Data Analysis, 2nd edition, 2017. O'Reilly Media.

Official Python documentation and tutorials: https://docs.python.org/3/tutorial/index.html

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mo 05.10.2020 10:08