Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions
Seiten
2019
McGraw-Hill Education (Verlag)
978-1-260-45277-8 (ISBN)
McGraw-Hill Education (Verlag)
978-1-260-45277-8 (ISBN)
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.
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.
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.
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
| Erscheinungsdatum | 22.08.2019 |
|---|---|
| Verlagsort | OH |
| Sprache | englisch |
| Maße | 198 x 241 mm |
| Gewicht | 726 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Finanz- / Wirtschaftsmathematik |
| Wirtschaft ► Betriebswirtschaft / Management ► Allgemeines / Lexika | |
| Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management | |
| ISBN-10 | 1-260-45277-8 / 1260452778 |
| ISBN-13 | 978-1-260-45277-8 / 9781260452778 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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