The course covers practical applications of data science to finance. The topics include:financial data management (sampling, labeling, weighting, fractional differentiation), modelling (ensemble methods, cross-validating (CV) in finance, feature importance, hyper-parameter tuning, backtesting (bet sizing, historical backtests, synthetic data, strategy risk), ML-based asset allocation, financial features (structural breaks, entropy features, microstructural features), how to design, develop and implement quantitative investment strategies, alpha models: momentum, value and quality factors, backtesting, risk models and mean-variance optimization, transaction costs modeling, market impact estimation, trade execution.