Financial Data Science
Cambridge University Press (Verlag)
978-1-009-43224-5 (ISBN)
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.
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 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 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 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.
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.
| Erscheinungsdatum | 19.07.2025 |
|---|---|
| Zusatzinfo | Worked examples or Exercises |
| Verlagsort | Cambridge |
| Sprache | englisch |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Technik ► Nachrichtentechnik | |
| Wirtschaft ► Betriebswirtschaft / Management ► Finanzierung | |
| ISBN-10 | 1-009-43224-9 / 1009432249 |
| ISBN-13 | 978-1-009-43224-5 / 9781009432245 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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