Financial Data Science, co-authored by researchers from VinUniversity, Politecnico di Torino, and the University of California, Berkeley—including Giuseppe Calafiore, Laurent El Ghaoui, Giulia Fracastoro, and Alicia Tsai—is a comprehensive Cambridge University Press textbook that bridges the disciplines of data science and quantitative finance. Reflecting VinUniversity's commitment to world-class research and education, the book offers a rigorous yet accessible introduction to machine learning techniques applied to real-world financial problems. Its 13 substantive chapters progress from foundational topics—data representation, statistical modeling, and principal component analysis—through advanced methods including neural networks, deep learning, nonlinear classifiers, and natural language processing for text analytics. The book also addresses portfolio optimization, financial networks, and clustering, supported by over 50 case studies and hands-on MATLAB and Python implementations. With more than 180 end-of-chapter exercises, it serves both graduate students and practicing quantitative finance professionals.