What you'll learn:
- Optimize for the highest Sharpe ratio in a real data portfolio using Excel´s Solver Add-in and R´s fPortfolio package
- Understand and Operationalize Markowitz´s Portfolio Theory
- Calculate Variance and Sharpe ratio for a twenty-asset portfolio
- Compute Covariance and Correlation of two assets
- Calculate Value at Risk (VaR) of a Portfolio
- Learn basic Vector Algebra (Matrix Multiplication)
Portfolio Management & Optimization: Excel, R, Python, AI is a practical course that teaches you how to build and optimize investment portfolios using real market data—so you can make better decisions about risk, return, diversification, and portfolio construction.
You will learn the core logic of Modern Portfolio Theory (MPT / Markowitz) and apply it step-by-step to compute portfolio metrics (expected return, volatility, correlation, covariance) and build the Efficient Frontier. The course starts with intuitive, transparent workflows in Excel, and then scales to more automated and professional toolsets in R and Python.
What you will do in this course
Apply portfolio management principles to structure diversified portfolios
Master modern portfolio theory and the intuition behind optimization
Build the efficient frontier and interpret optimal portfolios
Optimize portfolios using Excel Solver (clear and hands-on)
Use R (fPortfolio) to automate portfolio optimization and compare results
Use Python (PyPortfolioOpt) to run modern optimization workflows efficiently
Leverage AI / ChatGPT as a productivity tool for structure, interpretation, validation, and faster iteration
What’s included
Step-by-step tutorials with a learning-by-doing approach
Downloadable resources: Excel files, R code, and Python scripts
Practice tasks (with solutions) to validate your progress and confidence
Who this course is for
This course is designed for students and professionals who want to strengthen their skills in portfolio management and portfolio optimization, whether for academic work, professional finance/analytics, or to build a more rigorous investment framework.
What students say
Deepakraja S.: “Awesome course… best for beginners who would like to start their quant career and understand portfolio theory.”
Etienne R.: “Very practical… helps investors structure a portfolio efficiently and go beyond Excel using R.”
Omar H.: “Exceptional course. Explained everything in a concise, clear and to the point manner.”
Ernest A.: “The video ‘Defining Stock Return & Risk’ helped me understand the importance of standard deviation.”
Marcelo A.: “This course was extremely helpful for my thesis. Clear, practical, and valuable—thank you, Professor.”
If your goal is to move from theory to execution—building optimized portfolios with Excel, R, Python, and AI—this course will take you there step by step.
