Amazon cover image
Image from Amazon.com

Data mining for business analytics: concepts, techniques and applications in R

By: Contributor(s): Material type: TextTextPublication details: Wiley 2018 HobokenDescription: xxix, 544 pISBN:
  • 9781118879368
Subject(s): DDC classification:
  • 658.403802856312 S4D2
Summary: 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 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.” http://as.wiley.com/WileyCDA/WileyTitle/productCd-1118879368.html
List(s) this item appears in: IT and Decision Science
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
Holdings
Item type Current library Item location Collection Shelving location Call number Status Date due Barcode
Books Vikram Sarabhai Library Rack 38-A / Slot 2127 (2nd Floor, East Wing) Non-fiction General Stacks 658.403802856312 S4D2 (Browse shelf(Opens below)) Available 195559

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

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.”

http://as.wiley.com/WileyCDA/WileyTitle/productCd-1118879368.html

There are no comments on this title.

to post a comment.