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

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 serve").

Anmeldung von Mo 14.09.2020 09:00 bis Mi 23.09.2020 12:00
Anmeldung von Mo 28.09.2020 09:00 bis Mi 30.09.2020 12:00
Abmeldung bis Sa 31.10.2020 12:00

Details

max. 35 Teilnehmer*innen

Sprache: Englisch

Lehrende

Christian Westheide

Termine (iCal) - nächster Termin ist mit N markiert

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

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.
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.

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

The course will be taught via video conferencing.

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, 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

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mo 05.10.2020 10:08