Your cart is empty.
Keep shoppingPortfolio Management & Optimization: Excel, R, Python, AI
Role Play
24,264 students
Portfolio Management & Optimization: Excel, R, Python, AI
Portfolio management, modern portfolio theory, efficient frontier, Excel Solver, R fPortfolio, PyPortfolioOpt, ChatGPT
Last updated 1/2026
English
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)
Explore related topics
Course content
4 sections • 48 lectures • 4h 50m total length
Requirements
- Basic knowledge of Microsoft Excel: Familiarity with spreadsheet functions and formulas will be helpful for understanding and implementing portfolio optimization techniques using Excel's Solver Add-in.
- Basic understanding of R programming (Not mandatory): Although not mandatory, having a basic knowledge of R programming will enable you to effectively utilize R's fPortfolio package for portfolio optimization.
- Desire to learn and optimize investment portfolios: A strong interest in investment management and a willingness to learn and apply portfolio optimization techniques will enhance your experience and enable you to derive maximum benefit from the course.
- This course is designed to cater to both beginners and experienced individuals interested in portfolio optimization. The prerequisites are kept at a minimum to ensure accessibility and provide an opportunity for beginners to grasp the concepts and techniques covered.
Description
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.
Who this course is for:
- This course is designed for individuals who are interested in investment management and optimizing investment portfolios. The content is suitable for the following students:
- Finance professionals: Financial analysts, portfolio managers, investment advisors, and professionals working in the asset management industry who want to enhance their knowledge and skills in portfolio optimization techniques.
- Students pursuing finance or related degrees: Undergraduate and graduate students studying finance, economics, or other quantitative disciplines who want to gain a practical understanding of portfolio optimization methods and apply them in their academic or professional careers. This course has been highly beneficial for students pursuing finance master's programs at prestigious universities, as it complements their curriculum and helps them successfully complete their postgraduate studies.
- Quantitative analysts: Individuals with a background in quantitative analysis who want to expand their expertise in portfolio optimization using Excel's Solver Add-in and R's fPortfolio package.
- Individuals interested in personal finance: Individuals who are keen on managing their own investment portfolios and want to learn effective strategies for optimizing risk and return.
- Investment enthusiasts: Anyone with a general interest in investment management and a desire to understand the concepts and techniques behind portfolio optimization.