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Data Mining for Business Intelligence: Concepts, Techniques, and Applications
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Lieu où se trouve l'objet : Las Vegas, Nevada, États-Unis
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Numéro de l'objet eBay :156737190024
Caractéristiques de l'objet
- État
- Brand
- Unbranded
- Book Title
- Data Mining for Business Intelligence: Concepts, Techniques, and
- MPN
- Does not apply
- ISBN
- 9781118879368
À propos de ce produit
Product Identifiers
Publisher
Wiley & Sons, Incorporated, John
ISBN-10
1118879368
ISBN-13
9781118879368
eBay Product ID (ePID)
234971365
Product Key Features
Number of Pages
576 Pages
Publication Name
Data Mining for Business Analytics : concepts, Techniques, and Applications in R
Language
English
Publication Year
2017
Subject
Probability & Statistics / General, Databases / Data Mining, Enterprise Applications / General, Business Mathematics
Type
Textbook
Subject Area
Mathematics, Computers, Business & Economics
Format
Hardcover
Dimensions
Item Height
1.2 in
Item Weight
47.3 Oz
Item Length
10.1 in
Item Width
6.9 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2017-024503
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
658.05
Synopsis
Data Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and methods, using R software for illustration Readers will learn how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities. This is the fifth version of this successful text, and the first using R. 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: Two new co-authors, Inbal Yahav and Casey Lichtendahl, who bring both expertise teaching business analytics courses using R, and data mining consulting experience in business and government 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 www.dataminingbook.com Data Mining for Business Analytics: Concepts, Techniques, and Applications in R 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, Data Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and methods, using R software for illustration Readers will learn how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities. This is the fifth version of this successful text, and the first using R. 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: Two new co-authors, Inbal Yahav and Casey Lichtendahl, who bring both expertise teaching business analytics courses using R, and data mining consulting experience in business and government 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 www.dataminingbook.com Data Mining for Business Analytics: Concepts, Techniques, and Applications in R 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.
LC Classification Number
HF5548.2
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