Taddy, Matt

Business data science: combining machine learning and economics to optimize, automate, and accelerate business decisions - Chicago McGraw Hill Education 2019 - xii, 331 p. Includes bibliographical references and index

Table of contents

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

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.



Machine learning
Decision making
Econometric models

658.4033 / T2B8

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