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

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work
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. 35 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

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

Information

Aims, contents and method of the course

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.

Assessment and permitted materials

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

Minimum requirements and assessment criteria

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

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

Examination topics

All material covered in class.

Reading list

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

Association in the course directory

Last modified: Mo 08.05.2023 15:26