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

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften

Prüfungsimmanente Lehrveranstaltung

Moodle

An/Abmeldung

Anmeldung von Mo 10.02.2020 09:00 bis Mi 19.02.2020 12:00
Anmeldung von Di 25.02.2020 09:00 bis Mi 26.02.2020 12:00
Abmeldung bis Do 30.04.2020 23:59

Details

max. 35 Teilnehmer*innen

Sprache: Englisch

Lehrende

Christian Westheide

Termine (iCal) - nächster Termin ist mit N markiert

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

    
    Dienstag
    03.03.
    13:15 - 16:30
    PC-Seminarraum 5  Oskar-Morgenstern-Platz 1  1.Untergeschoß
    
    Dienstag
    10.03.
    13:15 - 16:30
    PC-Seminarraum 5  Oskar-Morgenstern-Platz 1  1.Untergeschoß
    
    Dienstag
    17.03.
    13:15 - 16:30
    PC-Seminarraum 5  Oskar-Morgenstern-Platz 1  1.Untergeschoß
    
    Dienstag
    24.03.
    13:15 - 16:30
    PC-Seminarraum 5  Oskar-Morgenstern-Platz 1  1.Untergeschoß
    
    Dienstag
    31.03.
    13:15 - 16:30
    PC-Seminarraum 5  Oskar-Morgenstern-Platz 1  1.Untergeschoß
    
    Dienstag
    21.04.
    13:15 - 16:30
    PC-Seminarraum 5  Oskar-Morgenstern-Platz 1  1.Untergeschoß
    
    Dienstag
    28.04.
    13:15 - 16:30
    PC-Seminarraum 5  Oskar-Morgenstern-Platz 1  1.Untergeschoß

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. 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.

Art der Leistungskontrolle und erlaubte Hilfsmittel

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

Mindestanforderungen und Beurteilungsmaßstab

40% homework exercises
20% class participation
40% 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

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.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mo 07.09.2020 15:19