MFE1109 — Programming for Quantitative Risk Management

This course introduces the fundamentals of two powerful programming languages: Python and R, specifically tailored for applications in quantitative finance and risk management. It provides a solid foundation in programming principles and the core libraries essential for financial data analysis, modeling, and simulation.

The primary goal is to equip students with the practical skills to implement quantitative models, automate risk analysis tasks, and apply these skills to financial modeling, risk assessment, and data-driven decision-making.

Key topics covered include:

Python (Weeks 1-11): Fundamental syntax, data structures (lists, dictionaries), control flow, modular programming with functions and classes, file handling, and scientific computing with NumPy.

R (Weeks 12-14): R ecosystem setup, data wrangling with tidyverse (dplyr), visualization with ggplot2, and reproducible reporting with Quarto/R Markdown.

Attendance: Students are required to attend at least 80% of all classroom hours to be eligible for the final examination. An attendance score of at least 5.0 is required.

Preparation: Students are expected to read all required materials before class and complete all homework assignments on time.

Participation: Active participation in class discussions, exercises, and group work is strongly encouraged.

Conduct: Maintain a professional and respectful learning environment. Do not disturb others. The use of mobile phones for non-class purposes is prohibited. Laptops are permitted for note-taking and in-class exercises only.