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Full 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.
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.
