Bridging Quantitative Finance and Machine Intelligence: A Principled Framework for Data-Driven Decision-Making in Modern Financial Systems
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.
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Confidently analyze, interpret and act on financial data with this practical introduction to the fundamentals of financial data science. Master the fundamentals with step-by-step introductions to core topics will equip you with a solid foundation for applying data science techniques to real-world complex financial problems. Extract meaningful insights as you learn how to use data to lead informed, data-driven decisions, with over 50 examples and case studies and hands-on Matlab and Python code. Explore cutting-edge techniques and tools in machine learning for financial data analysis, including deep learning and natural language processing. Accessible to readers without a specialized background in finance or machine learning, and including coverage of data representation and visualization, data models and estimation, principal component analysis, clustering methods, optimization tools, mean/variance portfolio optimization and financial networks, this is the ideal introduction for financial services professionals, and graduate students in finance and data science.