This comprehensive guide to the world of financial data modeling and portfoliodesign is a must-read for anyone looking to understand and apply portfolio optimizationin a practical context. It bridges the gap between mathematical formulations andthe design of practical numerical algorithms. It explores a range of methods, from basic time series models to cutting-edge financial graph estimation approaches. The portfolio formulations span from Markowitz's original 1952 mean–variance portfolio to more advanced formulations, including downside risk portfolios, drawdown portfolios, risk parity portfolios, robust portfolios, bootstrapped portfolios, index tracking, pairs trading, and deep-learning portfolios. Enriched with a remarkable collection of numerical experiments and more than 200 figures, this is a valuable resource for researchers and finance industry practitioners. With slides, R and Python code examples, and exercise solutions available online, it serves as a textbook for portfolio optimization and financial data modeling courses, at advanced undergraduate and graduate level.