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Finance professionals and graduate students in financial engineering, business, and data science alike will learn to confidently analyze, interpret and act on financial data with this practical introduction to the fundamentals of financial data science. Includes Python and Matlab examples, and real-world case studies and exercises.
List 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.
About the author
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.
