Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions

Author:   Matt Taddy
Publisher:   McGraw-Hill Education
ISBN:  

9781260452778


Pages:   352
Publication Date:   18 August 2019
Format:   Hardback
Availability:   Available To Order   Availability explained
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Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions


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Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. 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. 

Full Product Details

Author:   Matt Taddy
Publisher:   McGraw-Hill Education
Imprint:   McGraw-Hill Education
Dimensions:   Width: 19.80cm , Height: 2.80cm , Length: 24.10cm
Weight:   0.726kg
ISBN:  

9781260452778


ISBN 10:   1260452778
Pages:   352
Publication Date:   18 August 2019
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Available To Order   Availability explained
We have confirmation that this item is in stock with the supplier. It will be ordered in for you and dispatched immediately.

Table of Contents

Preface Introduction 1 Uncertainty 2 Regression 3 Regularization 4 Classification 5 Experiments 6 Controls 7 Factorization 8 Text as Data 9 Nonparametrics 10 Artificial Intelligence Bibliography Index

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Matt Taddy was from 2008-2018 a Professor of Econometrics and Statistics at the University of Chicago Booth School of Business, where he developed their Data Science curriculum. Prior to and while at Chicago Booth, he has also worked in a variety of industry positions including as a Principal Researcher at Microsoft and a research fellow at eBay. He left Chicago in 2018 to join Amazon as a Vice President.

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