This course is designed for finance students and investment professionals who want a practical understanding of how to evaluate investment risks and returns using Python.
Starting from the foundational concepts of risk and return, the course walks you through step-by-step calculations using real stock data. You will learn how to measure different types of returns, understand the statistical properties of return distributions, and apply formal normality tests, all using Python's scientific computing ecosystem.
What You Will Learn
By the end of this course, you will be able to:
- Calculate simple (discrete) and logarithmic returns from stock price data
- Visualise the distribution of returns using histograms
- Understand and compute the four moments of a distribution: mean, variance, skewness, and kurtosis
- Test whether financial returns follow a normal distribution using the Shapiro-Wilk test
- Apply these techniques to real-world stock data using
pandas,numpy,scipy,matplotlib, andseaborn
These skills will prepare you for more complex areas of portfolio risk management.
Prerequisites
A basic familiarity with Python and introductory statistics is helpful but not required. The course introduces every concept from first principles.
Data and Code
All code examples use a sample dataset (stock_data.csv) included with the course
materials. A complete Jupyter notebook (risk-return.ipynb) is also provided for
interactive exploration. Keep both files in the same folder before running any code.