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010   $a9781484225141
010   $a1484225147
024 7 $a10.1007/978-1-4842-2514-1
035   $a(CKB)3710000001000980
035   $a(DE-He213)978-1-4842-2514-1
035   $a(MiAaPQ)EBC4774045
035   $a(CaSebORM)9781484225141
035   $a(PPN)197452892
035   $a(OCoLC)1076490733
035   $a(OCoLC)on1076490733
035   $a(EXLCZ)993710000001000980
100   $a20161227d2017      u|         0
101 0 $aeng
135   $aurnn#008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aBusiness Analytics Using R - A Practical Approach /$fby Umesh R Hodeghatta, Umesha Nayak
205   $a1st ed. 2017.
210  1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2017.
215   $a1 online resource (XVII, 280 p. 278 illus.)
311 08$a9781484225134 
311 08$a1484225139 
320   $aIncludes bibliographical references and index.
327   $aOverview of business analytics -- Introduction to R -- R for data analysis -- Introduction to descriptive analytics -- Business analytics process and data exploration -- Supervised machine learning : classification -- Unsupervised machine learning -- Simple linear regression -- Multiple linear regression -- Logistic regression -- Big data analysis : introduction and future trends.
330   $aLearn the fundamental aspects of the business statistics, data mining, and machine learning techniques required to understand the huge amount of data generated by your organization. This book explains practical business analytics through examples, covers the steps involved in using it correctly, and shows you the context in which a particular technique does not make sense. Further, Practical Business Analytics using R helps you understand specific issues faced by organizations and how the solutions to these issues can be facilitated by business analytics. This book will discuss and explore the following through examples and case studies: An introduction to R: data management and R functions The architecture, framework, and life cycle of a business analytics project Descriptive analytics using R: descriptive statistics and data cleaning Data mining: classification, association rules, and clustering Predictive analytics: simple regression, multiple regression, and logistic regression This book includes case studies on important business analytic techniques, such as classification, association, clustering, and regression. The R language is the statistical tool used to demonstrate the concepts throughout the book. You will: ? Write R programs to handle data ? Build analytical models and draw useful inferences from them ? Discover the basic concepts of data mining and machine learning ? Carry out predictive modeling ? Define a business issue as an analytical problem.
606   $aBig data
606   $aComputer programming
606   $aProgramming languages (Electronic computers)
606   $aData mining
606   $aInformation storage and retrieval
606   $aMathematical statistics
606   $aR (Computer program language)
606   $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120
606   $aProgramming Techniques$3https://scigraph.springernature.com/ontologies/product-market-codes/I14010
606   $aProgramming Languages, Compilers, Interpreters$3https://scigraph.springernature.com/ontologies/product-market-codes/I14037
606   $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030
606   $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032
606   $aProbability and Statistics in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17036
615  0$aBig data.
615  0$aComputer programming.
615  0$aProgramming languages (Electronic computers)
615  0$aData mining.
615  0$aInformation storage and retrieval.
615  0$aMathematical statistics.
615  0$aR (Computer program language)
615 14$aBig Data.
615 24$aProgramming Techniques.
615 24$aProgramming Languages, Compilers, Interpreters.
615 24$aData Mining and Knowledge Discovery.
615 24$aInformation Storage and Retrieval.
615 24$aProbability and Statistics in Computer Science.
676   $a658.054
700   $aHodeghatta$b Umesh R$4aut$4http://id.loc.gov/vocabulary/relators/aut$0900654
702   $aNayak$b Umesha$4aut$4http://id.loc.gov/vocabulary/relators/aut
801  0$bUMI
801  1$bUMI
906   $aBOOK
912   $a9910157379203321
996   $aBusiness Analytics Using R - A Practical Approach$92012801
997   $aUNINA