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Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions - Hardcover | Diverse Reads

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

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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.

ISBN-13: 9781260452778

Media Type: Hardcover

Publisher: McGraw Hill LLC

Publication Date: 08-23-2019

Pages: 352

Product Dimensions: 7.60(w) x 9.40(h) x 1.20(d)

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.

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