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Financial Data Science

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Financial Data Science

Financial Data Science

Giuseppe Calafiore, Politecnico di Torino
Laurent El Ghaoui, University of California, Berkeley
Giulia Fracastoro, Politecnico di Torino
Alicia Tsai, University of California, Berkeley
June 2025
Not yet published - available from June 2025
Hardback
9781009432245
c.
£59.99
GBP
Hardback
    Description

    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.

    • Includes over 180 individual end-of-chapter problems to consolidate understanding
    • Written as a step-by-step introduction to machine learning concepts for financial applications, presented from first principles
    • A versatile textbook which can also serve as a self-learning manual for perspective quantitative finance professionals needing to acquire the basic tools of the trade

    Product details

    June 2025
    Hardback
    9781009432245
    414 pages
    254 × 203 mm
    Not yet published - available from June 2025

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    Contents

    Table of Contents

    • 1. Preface
    • 2. Data representation and visualization
    • 3. Data models and estimation
    • 4. Principle component analysis
    • 5. Clustering methods
    • 6. Linear regression models
    • 7. Linear classifers
    • 8. Nonlinear classifiers and kernel methods
    • 9. Neural networks and deep learning
    • 10. Optimization tools
    • 11. Mean/variance portfolio optimization
    • 12. Beyond the mean/variance model
    • 13. Financial networks
    • 14. Text analytics
    • Index.
      • Show more
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    Additional Information
    About the authors
      Authors
    • Giuseppe Calafiore , Politecnico di Torino

      Giuseppe C. Calafiore is a Professor of Automatic Control at the Electronics and Telecommunications Department at Politecnico di Torino, where he coordinates the Control Systems and Data Science group, and a former Visiting Professor at the University of California, Berkeley, where he co-taught graduate courses in financial data science. He is a co-author of Optimization Models (2014), and a Fellow of the IEEE.

    • Laurent El Ghaoui , University of California, Berkeley

      Laurent El Ghaoui is Vice-Provost of Research and Innovation, and Dean of Engineering and Computer Science, at Vin University. He is a former Professor of Electrical Engineering and Computer Science at the University of California, Berkeley, where he taught topics in data science and optimization models within the Haas Business School Master of Financial Engineering programme. He is a co-author of Optimization Models (2014).

    • Giulia Fracastoro , Politecnico di Torino

      Giulia Fracastoro is an Assistant Professor at the Electronics and Telecommunications Department at Politecnico di Torino. In 2017, she obtained her Ph.D. degree in Electronics and Telecommunications Engineering from Politecnico di Torino with a thesis on design and optimization of graph transform for image and video compression. Her main research interests are graph signal processing and neural networks on graph-structured data.

    • Alicia Tsai , University of California, Berkeley

      Alicia Y. Tsai is a Research Engineer at Google DeepMind. She obtained her Ph.D. in Computer Sciences from the University of California, Berkeley. Her main research interests are optimization, natural language processing, and machine learning. She is also a founding board member of the Taiwan Data Science Association and the founder of Women in Data Science (WiDS) Taipei.

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