Universität Wien FIND

040164 KU Python for Finance I (MA) (2020W)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work


Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).


max. 35 participants
Language: English


Classes (iCal) - next class is marked with N

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


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.
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.

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. The final exam will take place on Moodle on January, 25.

The course will be taught via video conferencing.

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, 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

Association in the course directory

Last modified: Mo 05.10.2020 10:08