This course is designed to provide a comprehensive understanding of portfolio management using advanced techniques and tools. The course covers the basics of Python programming language and its applications in financial analysis, including data structures using NumPy and Pandas libraries. The course then delves into portfolio optimization and efficient set models, including mean-variance portfolio optimization, mean-semivariance, mean-CVaR, mean-CDaR, and the Black-Litterman model. The course also covers the uniform portfolio, the portfolio with uniform risk contributions, and the Hierarchical Risk Parity portfolio. Additionally, the course covers measuring portfolio performance and risk and various approaches to portfolio rebalancing, including alarms. The course also covers the basics of algorithmic trading and automated trading systems, including an example of two moving averages crossover system. Overall, this course provides a solid foundation for advanced portfolio management and equips learners with the necessary skills to succeed in this field.