Universität Wien FIND

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040031 KU Python for Finance I (MA) (2020S)

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

Registration/Deregistration

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

Details

max. 35 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Home learning from March 17. The final exam will take place online on April 28. See the announcements on Moodle.

Tuesday 03.03. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday 10.03. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday 17.03. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday 24.03. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday 31.03. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday 21.04. 13:15 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday 28.04. 13:15 - 16:30 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.
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. Introduction to Programming and Python
2. Numerical Computing with NumPy
3. Data Analysis with pandas

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

Assessment and permitted materials

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

Minimum requirements and assessment criteria

40% homework exercises
20% class participation
40% 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

Others (besides official Python documentation and tutorials):

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

Hilpisch, Yves, Python for Finance: Mastering Data-Driven Finance, 2018, O’Reilly Publishing.

Association in the course directory

Last modified: Mo 07.09.2020 15:19