040031 UE Python for Finance I (MA) (2023S)
Continuous assessment of course work
Labels
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 Mo 13.02.2023 09:00 to We 22.02.2023 12:00
- Registration is open from Mo 27.02.2023 09:00 to Tu 28.02.2023 12:00
- Deregistration possible until Fr 17.03.2023 23:59
Details
max. 35 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
Tuesday
07.03.
13:15 - 16:30
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday
14.03.
13:15 - 16:30
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday
21.03.
13:15 - 16:30
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday
28.03.
13:15 - 16:30
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday
18.04.
13:15 - 16:30
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday
25.04.
13:15 - 16:30
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Tuesday
02.05.
13:15 - 14:45
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Information
Aims, contents and method of the course
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 examMinimum requirement for a positive grade: a total of 50%.
10% active class participation
30% final examMinimum 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, 2021. https://www.kevinsheppard.com/files/teaching/python/notes/python_introduction_2021.pdfMcKenney, 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 08.05.2023 15:26
2. Numerical Computing with NumPy
3. Data Analysis with pandas
4. Regression Analysis with statsmodels and linearmodelsFurthermore, data visualization with matplotlib will be part of all chapters.