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Business Analytics by Johannes Ledolter: New
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eBay item number:284413440981
Item specifics
- Condition
- Brand New: A new, unread, unused book in perfect condition with no missing or damaged pages. See all condition definitionsopens in a new window or tab
- Book Title
- Business Analytics
- Publication Date
- 2013-05-28
- Pages
- 368
- ISBN
- 9781118447147
About this product
Product Identifiers
Publisher
Wiley & Sons, Incorporated, John
ISBN-10
111844714X
ISBN-13
9781118447147
eBay Product ID (ePID)
5038456613
Product Key Features
Number of Pages
368 Pages
Language
English
Publication Name
Data Mining and Business Analytics with R
Subject
Programming Languages / General, Probability & Statistics / General, Databases / Data Mining, Statistics
Publication Year
2013
Type
Textbook
Subject Area
Mathematics, Computers, Business & Economics
Format
Hardcover
Dimensions
Item Height
1 in
Item Weight
24.1 Oz
Item Length
9.3 in
Item Width
6.2 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2013-000330
Dewey Edition
23
Reviews
"I first taught a Ph.D. level course in business applications of data mining 10 years ago. I regularly search the web, looking for business-oriented data mining books, and this is the first one I have found that is suitable for an MS in business analytics. I plan to use it. Anyone who teaches such a class and is inclined toward R should consider this text." ( Journal of the American Statistical Association , 1 January 2014)
Illustrated
Yes
Dewey Decimal
006.312
Synopsis
Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents: - A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools - Illustrations of how to use the outlined concepts in real-world situations - Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials - Numerous exercises to help readers with computing skills and deepen their understanding of the material Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences., Showcases R's critical role in the world of business Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible robust computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents: A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools Illustrations of how to use the outlined concepts in real-world situations Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials Numerous exercises to help readers with computing skills and deepen their understanding of the material Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences., Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets., Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents: A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools Illustrations of how to use the outlined concepts in real-world situations Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials Numerous exercises to help readers with computing skills and deepen their understanding of the material Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.
LC Classification Number
QA76.9.D343L44 2013
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Seller feedback (518,627)
- eBay automated Feedback- Feedback left by buyer.Past monthOrder completed successfully – tracked and on time
- eBay automated Feedback- Feedback left by buyer.Past monthOrder completed successfully – tracked and on time
- eBay automated Feedback- Feedback left by buyer.Past monthOrder completed successfully – tracked and on time