Financial Data Science with SAS - Odusami, Babatunde O
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Explore financial data science using SAS. Financial Data Science with SAS provides readers with a comprehensive explanation of the theoretical and practical implementation of the various types of analytical techniques and quantitative tools that are used in the financial services industry. This book shows readers how to implement data visualization, simulation, statistical predictive models, machine learning models, and financial optimizations using real-world examples in the SAS Analytics environment. Each chapter ends with practice exercises that include use case scenarios to allow readers…mehr

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Produktbeschreibung
Explore financial data science using SAS. Financial Data Science with SAS provides readers with a comprehensive explanation of the theoretical and practical implementation of the various types of analytical techniques and quantitative tools that are used in the financial services industry. This book shows readers how to implement data visualization, simulation, statistical predictive models, machine learning models, and financial optimizations using real-world examples in the SAS Analytics environment. Each chapter ends with practice exercises that include use case scenarios to allow readers to test their knowledge. Designed for university students and financial professionals interested in boosting their data science skills, Financial Data Science with SAS is an essential reference guide for understanding how data science is used in the financial services industry and for learning how to use SAS to solve complex business problems.
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Autorenporträt
Babatunde Odusami, PhD, CFA, is a professor of finance at Widener University in Chester, PA. Alongside his academic roles, he is involved in investment management and governance, corporate governance, and consulting. He received an MBA with a concentration in management information systems, as well as an MS and PhD in financial economics from the University of New Orleans. He is also a CFA charter holder. His research interests are in statistical and machine learning models that can explain how the values and risks of financial assets and portfolios evolve. His commentaries on financial topics are periodically featured in digital and TV media.