Universität Wien

040031 UE Python for Finance I (MA) (2023S)

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

An/Abmeldung

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

Details

max. 35 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

  • Dienstag 07.03. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 14.03. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 21.03. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 28.03. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 18.04. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 25.04. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 02.05. 13:15 - 14:45 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß

Information

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. Prior exposure to econometrics is useful though not strictly necessary.

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. The course inevitably requires a steep learning curve.

Main Topics of the Course:

1. Python and Programming Basics
2. Numerical Computing with NumPy
3. Data Analysis with pandas
4. Regression Analysis with statsmodels and linearmodels

Furthermore, data visualization with matplotlib will be part of all chapters.

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.

Mindestanforderungen und Beurteilungsmaßstab

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

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

Prüfungsstoff

All material covered in class.

Literatur

Main reference:

Sheppard, Kevin. Introduction to Python for Econometrics, Statistics and Data Analysis, 2021. https://www.kevinsheppard.com/files/teaching/python/notes/python_introduction_2021.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 08.05.2023 15:26