R for Business Analytics [electronic resource] / by A Ohri.

By: Ohri, A [author.]Contributor(s): SpringerLink (Online service)Material type: TextTextPublisher: New York, NY : Springer New York : Imprint: Springer, 2013Description: XVIII, 312 p. 206 illus., 160 illus. in color. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781461443438Subject(s): Statistics | Statistics | Statistics, general | Statistics for Business/Economics/Mathematical Finance/Insurance | Statistics and Computing/Statistics ProgramsAdditional physical formats: Printed edition:: No titleDDC classification: 519.5 LOC classification: QA276-280Online resources: Texto completo
Contents:
Why 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.
In: Springer eBooksSummary: R 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.  .
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Item type Current location Shelving location Call number Status Date due Barcode Item holds
Springer (Colección 2013) Springer (Colección 2013) BIBLIOTECA GENERAL
Matemáticas y Estadística Matemáticas y Estadística (Browse shelf) Available
Total holds: 0

Why 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.

R 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.  .

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