Overview
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.
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.
- | Author: Alicia Tsai, Laurent El Ghaoui, Giuseppe Calafiore, Giulia Fracastoro
- | Publisher: Cambridge University Press
- | Publication Date: Jul 17, 2025
- | Number of Pages:
- | Language:
- | Binding: Hardback
- | ISBN-13: 9781009432245
- | ISBN-10: 1009432249
