Introduction to Portfolio Construction and Analysis with Python Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
A comprehensive course that effectively combines financial theory with practical Python implementation for portfolio construction and analysis. This beginner-level course spans approximately 22 hours of content across four core modules, with hands-on programming exercises integrated throughout. Learners will gain both theoretical understanding and practical coding skills needed to build, analyze, and optimize investment portfolios using Python. The course concludes with a final project that synthesizes key concepts and techniques.
Module 1: Analysing Returns
Estimated time: 6 hours
- Understanding financial returns and their measurement
- Calculating risk-adjusted performance metrics
- Exploring Max Drawdown, Value at Risk (VaR), and Conditional VaR (CVaR)
- Implementing return analysis in Python through hands-on lab sessions
Module 2: An Introduction to Portfolio Optimization
Estimated time: 4 hours
- Introduction to portfolio optimization concepts
- Understanding the efficient frontier
- Assessing the trade-off between risk and return
- Implementing optimization techniques using Python
Module 3: Beyond Diversification
Estimated time: 6 hours
- Exploring factor models and style analysis
- Understanding limitations of traditional diversification
- Implementing advanced diversification techniques in Python
Module 4: Introduction to Asset-Liability Management
Estimated time: 6 hours
- Principles of asset-liability management (ALM)
- Strategies for managing financial risks associated with liabilities
- Implementing ALM strategies using Python
Module 5: Final Project
Estimated time: 6 hours
- Build a diversified portfolio using Python
- Apply risk and return estimation techniques
- Evaluate portfolio performance and compare strategies
Prerequisites
- Familiarity with basic financial concepts
- Introductory knowledge of Python programming
- Background in finance or programming recommended due to technical content
What You'll Be Able to Do After
- Gain an intuitive understanding of modern portfolio construction theory
- Write custom Python code to estimate risk and return parameters
- Utilize Python optimization libraries to build diversified portfolios
- Build custom utilities to test and compare portfolio strategies
- Apply asset-liability management principles using Python implementations