DE / EN

FIN 687: Python in Finance

Contents
This course equips students with the basics in Python to pursue quantitative seminar and Master's theses in finance.
After the programming fundamentals, data handling, visualization and analysis is discussed, as well as accessing and working with data sources typically used in the finance literature. This includes the fundamentals of web scraping, machine learning and working with large datasets. The course concludes with a case study, where the acquired skills are put into practice to address a financial research question.
Practical applications are given precedence over theoretical programming concepts. While the course is also suitable for students from other fields, the practical examples are drawn from the finance literature.

Learning outcomes
After the course, students should be able to start working independently on quantitative topics in the field of finance using the programming language Python. Students also acquire knowledge about data acquisition, transformation, visualization, and analysis, including regressions and machine learning techniques.

Necessary prerequisites

Recommended prerequisites
The number of participants is limited. We will prioritize students who are writing an empirical seminar thesis in the Finance Area in the semester in the allocation procedure.

Forms of teaching and learningContact hoursIndependent study time
Lecture with integrated exercise4 SWS2 SWS
ECTS credits2
Graded yes
Workload60h
LanguageEnglish
Form of assessmentTake home exam (pass/fail). Note that there is only one exam date per semester. A second attempt is only possible in the respective following semester.
Restricted admissionyes
Further informationhttps://www.bwl.uni-mannheim.de/en/ruenzi/teaching/master-courses/fin-687-python-in-finance
Examiner
Performing lecturer
Prof. Ruenzi hat kurze Haare und trägt einen Anzug. Er steht vor einer Sandsteinmauer.
Prof. Dr. Stefan Ruenzi
Sven Vahlpahl
Frequency of offeringSpring semester & fall semester
Duration of module 1 semester
Range of applicationM.Sc. MMM, M.Sc. Bus. Edu., M.Sc. Econ., M.Sc. Bus. Inf., M.Sc. Bus. Math., M.Sc. MMFACT
Preliminary course work
Program-specific Competency GoalsCG 1, CG 4
LiteratureThe course is based on the provided Google Colab notebooks.
Course outline
  • Fundamentals
  • Functions and packages
  • Data structures, visualization and data retrieval
  • Statistical analysis
  • Advanced methods