data mining business analytics von ledolter johannes (20 Ergebnisse)

Zustand: Fine. First Edition. Hardcover : pp. [iv] xi [xii] 351 [5 blank] : blue and green illustrated boards.

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Zustand: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.

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Buch. Zustand: Neu. Neuware - 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 materialData 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.

Hardcover. Zustand: new. Hardcover. 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 toolsIllustrations of how to use the outlined concepts in real-world situationsReadily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materialsNumerous 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Zustand: New. 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. Num Pages: 368 pages, Illustrations (some color). BIC Classification: KJMV3; UFM; UNF. Category: (P) Professional & Vocational. Dimension: 241 x 156 x 22. Weight in Grams: 736. . 2013. 1st Edition. Hardcover. . . . .

Zustand: New. pp. 368.

Zustand: New. pp. 368 Index.

Zustand: New. 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. Num Pages: 368 pages, Illustrations (some color). BIC Classification: KJMV3; UFM; UNF. Category: (P) Professional & Vocational. Dimension: 241 x 156 x 22. Weight in Grams: 736. . 2013. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.

Hardcover. Zustand: new. Hardcover. 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 toolsIllustrations of how to use the outlined concepts in real-world situationsReadily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materialsNumerous 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Zustand: Brand New. 1st edition. 368 pages. 9.53x6.38x0.79 inches. In Stock.

Hardcover. Zustand: new. Hardcover. 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 toolsIllustrations of how to use the outlined concepts in real-world situationsReadily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materialsNumerous 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

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Hardcover. Zustand: Brand New. 1st edition. 368 pages. 9.53x6.38x0.79 inches. In Stock. This item is printed on demand.