LEADER 03886nam 2200589 a 450 001 9910438147203321 005 20200520144314.0 010 $a1-283-62397-8 010 $a9786613936424 010 $a1-4614-4343-1 024 7 $a10.1007/978-1-4614-4343-8 035 $a(CKB)2670000000246539 035 $a(EBL)1030500 035 $a(OCoLC)810448947 035 $a(SSID)ssj0000767160 035 $a(PQKBManifestationID)11430336 035 $a(PQKBTitleCode)TC0000767160 035 $a(PQKBWorkID)10739991 035 $a(PQKB)11577527 035 $a(DE-He213)978-1-4614-4343-8 035 $a(MiAaPQ)EBC1030500 035 $a(PPN)168299941 035 $a(EXLCZ)992670000000246539 100 $a20120606d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aR for business analytics /$fA. Ohri 205 $a1st ed. 2013. 210 $aNew York $cSpringer$d2012 215 $a1 online resource (319 p.) 300 $aDescription based upon print version of record. 311 $a1-4614-4342-3 320 $aIncludes bibliographical references and index. 327 $aWhy R -- R Infrastructure -- R Interfaces -- Manipulating Data -- Exploring Data -- Building Regression Models -- Data Mining using R -- Clustering and Data Segmentation -- Forecasting and Time-Series Models -- Data Export and Output -- Optimizing your R Coding -- Additional Training Literature -- Appendix. 330 $aR for Business Analytics looks at some of the most common tasks performed by business analysts and helps the user navigate the wealth of information in R and its 4000 packages.  With this information the reader can select the packages that can help process the analytical tasks with minimum effort and maximum usefulness. The use of Graphical User Interfaces (GUI) is emphasized in this book to further cut down and bend the famous learning curve in learning R. This book is aimed to help you kick-start with analytics including chapters on data visualization, code examples on web analytics and social media analytics, clustering, regression models, text mining, data mining models and forecasting. The book tries to expose the reader to a breadth of business analytics topics without burying the user in needless depth. The included references and links allow the reader to pursue business analytics topics.    This book is aimed at business analysts with basic programming skills for using R for Business Analytics. Note the scope of the book is neither statistical theory nor graduate level research for statistics, but rather it is for business analytics practitioners. Business analytics (BA) refers to the field of exploration and investigation of data generated by businesses. Business Intelligence (BI) is the seamless dissemination of information through the organization, which primarily involves business metrics both past and current for the use of decision support in businesses. Data Mining (DM) is the process of discovering new patterns from large data using algorithms and statistical methods. To differentiate between the three, BI is mostly current reports, BA is models to predict and strategize and DM matches patterns in big data. The R statistical software is the fastest growing analytics platform in the world, and is established in both academia and corporations for robustness, reliability and accuracy.  . 606 $aR (Computer program language) 606 $aBusiness enterprises 615 0$aR (Computer program language) 615 0$aBusiness enterprises. 676 $a658.4/0302855133 676 $a658.4720285555 700 $aOhri$b A$01060276 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910438147203321 996 $aR for Business Analytics$92541884 997 $aUNINA