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001 978-1-4614-4343-8
003 DE-He213
005 20140220082814.0
007 cr nn 008mamaa
008 120913s2013 xxu| s |||| 0|eng d
020 _a9781461443438
_9978-1-4614-4343-8
024 7 _a10.1007/978-1-4614-4343-8
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aOhri, A.
_eauthor.
245 1 0 _aR for Business Analytics
_h[electronic resource] /
_cby A Ohri.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2013.
300 _aXVIII, 312 p. 206 illus., 160 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _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.
520 _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.  
650 0 _aStatistics.
650 0 _aMathematical statistics.
650 0 _aEconomics
_xStatistics.
650 1 4 _aStatistics.
650 2 4 _aStatistics, general.
650 2 4 _aStatistics for Business/Economics/Mathematical Finance/Insurance.
650 2 4 _aStatistics and Computing/Statistics Programs.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781461443421
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4614-4343-8
912 _aZDB-2-SMA
999 _c95116
_d95116