Machine Learning for Business Analytics: Concepts, Techniques and Applications with JMP Pro - Hardcover

Shmueli, Galit; Bruce, Peter C.; Stephens, Mia L.; Anandamurthy, Muralidhara; Patel, Nitin R.

Hardcover

ISBN 10: 1119903831 ISBN 13: 9781119903833

Publisher: Wiley, 2023

Synopsis

MACHINE LEARNING FOR BUSINESS ANALYTICS

An up-to-date introduction to a market-leading platform for data analysis and machine learning

Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, 2nd ed. offers an accessible and engaging introduction to machine learning. It provides concrete examples and case studies to educate new users and deepen existing users’ understanding of their data and their business. Fully updated to incorporate new topics and instructional material, this remains the only comprehensive introduction to this crucial set of analytical tools specifically tailored to the needs of businesses.

Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, 2nd ed. readers will also find:

• Updated material which improves the book’s usefulness as a reference for professionals beyond the classroom
• Four new chapters, covering topics including Text Mining and Responsible Data Science
• An updated companion website with data sets and other instructor resources: www.jmp.com/dataminingbook
• A guide to JMP Pro's new features and enhanced functionality

Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, 2nd ed. is ideal for students and instructors of business analytics and data mining classes, as well as data science practitioners and professionals in data-driven industries.

About this title

About the Author

Galit Shmueli, PhD is Distinguished Professor at National Tsing Hua University's Institute of Service Science. She has designed and instructed business analytics courses since 2004 at University of Maryland, Statistics.com, The Indian School of Business, and National Tsing Hua University, Taiwan.

Peter C. Bruce is Founder of the Institute for Statistics Education at Statistics.com, and Chief Learning Officer at Elder Research, Inc.

Mia L. Stephens, M.S. is an Advisory Product Manager with JMP, driving the product vision and roadmaps for JMP and JMP Pro.

Muralidhara Anandamurthy, PhD is an Academic Ambassador with JMP, overseeing technical support for academic users of JMP Pro.

Nitin R. Patel, PhD is cofounder and lead researcher at Cytel Inc. He is also a Fellow of the American Statistical Association and has served as a visiting professor at the Massachusetts Institute of Technology and Harvard University, among others.

From the Back Cover

An up-to-date introduction to a market-leading platform for data analysis and machine learning

Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, 2nd ed. offers an accessible and engaging introduction to machine learning. It provides concrete examples and case studies to educate new users and deepen existing users' understanding of their data and their business. Fully updated to incorporate new topics and instructional material, this remains the only comprehensive introduction to this crucial set of analytical tools specifically tailored to the needs of businesses.

Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, 2nd ed. readers will also find:

• Updated material which improves the book’s usefulness as a reference for professionals beyond the classroom
• Four new chapters, covering topics including Text Mining and Responsible Data Science
• An updated companion website with data sets and other instructor resources: www.jmp.com/dataminingbook
• A guide to JMP Pro's new features and enhanced functionality

Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, 2nd ed. is ideal for students and instructors of business analytics and data mining classes, as well as data science practitioners and professionals in data-driven industries.

Edition details

• Publisher: Wiley
• Publication date: 2023
• Language: English
• ISBN 10: 1119903831
• ISBN 13: 9781119903833
• Binding: Hardcover
• Edition number: 2
• Number of pages: 608