R for Business Analytics (Record no. 49362)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02301 a2200169 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250120115338.0 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781493942398 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 658.401 OHR |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Ohri, A |
| 245 ## - TITLE STATEMENT | |
| Title | R for Business Analytics |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher, distributor, etc | Springer |
| Place of publication, distribution, etc | New York |
| Date of publication, distribution, etc | 2013 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 312 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Business Analytics |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Data Mining |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Book |
| Source of classification or shelving scheme | Dewey Decimal Classification |
No items available.
