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040031 KU Python for Finance I (MA) (2021S)
Continuous assessment of course work
Labels
REMOTE
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
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
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, 2020. https://www.kevinsheppard.com/files/teaching/python/notes/python_introduction_2020.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 03.05.2021 11:07
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.