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Data Mining for Business Analytics: Concepts, Techniques and Applications in Python Hardcover – Nov. 5 2019

4.5 out of 5 stars 63 ratings

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Product description

From the Inside Flap

Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration

Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities.

This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes:

  • A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process
  • A new section on ethical issues in data mining
  • Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students
  • More than a dozen case studies demonstrating applications for the data mining techniques described
  • End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented
  • A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions

Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.

"This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject."
—GARETH M. JAMES, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R

From the Back Cover

Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration

Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities.

This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes:

  • A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process
  • A new section on ethical issues in data mining
  • Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students
  • More than a dozen case studies demonstrating applications for the data mining techniques described
  • End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented
  • A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions

Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.

"This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject."
—GARETH M. JAMES, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R

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Product details

  • Publisher ‏ : ‎ Wiley; 1st edition (Nov. 5 2019)
  • Language ‏ : ‎ English
  • Hardcover ‏ : ‎ 608 pages
  • ISBN-10 ‏ : ‎ 1119549841
  • ISBN-13 ‏ : ‎ 978-1119549840
  • Item weight ‏ : ‎ 1.1 kg
  • Dimensions ‏ : ‎ 18.29 x 3.05 x 25.65 cm
  • Customer Reviews:
    4.5 out of 5 stars 63 ratings

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4.5 out of 5 stars
4.5 out of 5
63 global ratings

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Ifeoluwa Akande
4.0 out of 5 stars Great learning experience reading this book!
Reviewed in the United Kingdom 🇬🇧 on July 5, 2020
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5.0 out of 5 stars Reading this book is like have a private tutor.
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Hanna's Mum
1.0 out of 5 stars Missing and Incomplete Formulas
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Hanna's Mum
1.0 out of 5 stars Missing and Incomplete Formulas
Reviewed in Australia 🇦🇺 on March 16, 2022
The formulas are missing graphics and symbols, making it difficult to use. It’s extremely disappointing given the high cost of the book. Unable to return as I’d made notes before realising the publishing errors.
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M
1.0 out of 5 stars Don't Buy The Kindle Version
Reviewed in the United States 🇺🇸 on March 24, 2020
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Jon Bowers
1.0 out of 5 stars Equations in physical copy are all jumbled and/or missing signs
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