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040031 KU Python for Finance I (MA) (2021S)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften

Continuous assessment of course work

REMOTE

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).

Registration is open from Th 11.02.2021 09:00 to Mo 22.02.2021 12:00
Registration is open from Th 25.02.2021 09:00 to Fr 26.02.2021 12:00
Deregistration possible until We 31.03.2021 23:59

Details

max. 50 participants

Language: English

Lecturers

Christian Westheide

Classes (iCal) - next class is marked with N

    
    Tuesday
    02.03.
    13:15 - 16:30
    Digital
    
    Tuesday
    09.03.
    13:15 - 16:30
    Digital
    
    Tuesday
    16.03.
    13:15 - 16:30
    Digital
    
    Tuesday
    23.03.
    13:15 - 16:30
    Digital
    
    Tuesday
    13.04.
    13:15 - 16:30
    Digital
    
    Tuesday
    20.04.
    13:15 - 16:30
    Digital
    
    Tuesday
    27.04.
    13:15 - 16:30
    Digital

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, 2020. https://www.kevinsheppard.com/files/teaching/python/notes/python_introduction_2020.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 03.05.2021 11:07