Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions (BUSINESS BOOKS) - Hardcover

Taddy, Matt

Hardcover

ISBN 10: 1260452778 ISBN 13: 9781260452778

Publisher: McGraw Hill, 2019

Synopsis

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Use machine learning to understand your customers, frame decisions, and drive value  


The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science.  Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: 


• Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling
• Understand how use ML tools in real world business problems, where causation matters more that correlation
• Solve data science programs by scripting in the R programming language


Today’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science. 

About this title

About the Author

Matt Taddy is a Chief Economist at Amazon and Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. He is a fellow of the Computation Institute and a Principal Researcher at Microsoft Research New England.

From the Back Cover

" Matt Taddy has written a thorough, thoughtful book on the statistics that underlie the use of big data. This is a fantastic resource, chock full of real applications, techniques, and insight. Unlike most machine learning texts, this book provides methods of extracting reliable understanding from data, addressing the problem that correlation is not causation."
--PRESTON MCAFEE, former Chief Economist and Corporate Vice President for Microsoft, and Professor and Executive Officer for the California Institute of Technology

" Drawing on his experience from his days as a star teacher at Chicago Booth and his work leading data science teams at Microsoft and Amazon, Matt Taddy has written a masterful book for MBAs, scientists, and engineers at modern companies. Weaving together concepts from statistics, machine learning, and social science, he has written a highly accessible text that is likely to become the standard in this area."
--GUIDO IMBENS, Professor of Economics at the Stanford Graduate School of Business, coauthor of Causal Inference for Statistics, Social, and Biomedical Sciences

" No one is better at combining insights from computer science, economics, and statistics to improve how businesses use their data. Everyone should read this book."
--JENS LUDWIG, McCormick Foundation Professor of Social Service Administration, Law and Public Policy and Director of the University of Chicago's Crime Lab

" Business Data Science brings together historically distinct disciplines to tackle a basic business reality: accurate predictions are not an end in themselves, but a means to taking high-quality actions. It reaches the current state of the art without requiring a strong data-science background. I recommend it to anyone interested in putting these ideas to practice."
--JON MCAULIFFE, Cofounder and Chief Investment Officer at The Voleon Group

" The most exciting data science book I have read in some time: current, modern, accessible, rigorous--and awesome."
--DIRK EDDELBUETTEL, R-package author and Clinical Professor of Statistics at University of Illinois

Edition details

• Publisher: McGraw Hill
• Publication date: 2019
• Language: English
• ISBN 10: 1260452778
• ISBN 13: 9781260452778
• Binding: Hardcover
• Number of pages: 352