Data Science Using Oracle Data Miner and Oracle R Enterprise - Das, sibanjan
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Automate the predictive analytics process using Oracle Data Miner and Oracle R Enterprise. This book talks about how both these technologies can provide a framework for in-database predictive analytics. You'll see a unified architecture and embedded workflow to automate various analytics steps such as data preprocessing, model creation, and storing final model output to tables. You'll take a deep dive into various statistical models commonly used in businesses and how they can be automated for predictive analytics using various SQL, PLSQL, ORE, ODM, and native R packages. You'll get to know…mehr

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Produktbeschreibung
Automate the predictive analytics process using Oracle Data Miner and Oracle R Enterprise. This book talks about how both these technologies can provide a framework for in-database predictive analytics. You'll see a unified architecture and embedded workflow to automate various analytics steps such as data preprocessing, model creation, and storing final model output to tables.
You'll take a deep dive into various statistical models commonly used in businesses and how they can be automated for predictive analytics using various SQL, PLSQL, ORE, ODM, and native R packages. You'll get to know various options available in the ODM workflow for driving automation. Also, you'll get an understanding of various ways to integrate ODM packages, ORE, and native R packages using PLSQL for automating the processes.

Data Science Automation Using Oracle Data Miner and Oracle R Enterprise starts with an introduction to business analytics, covering why automation is necessary and the level of complexity in automation at each analytic stage. Then, it focuses on how predictive analytics can be automated by using Oracle Data Miner and Oracle R Enterprise. Also, it explains when and why ODM and ORE are to be used together for automation.
The subsequent chapters detail various statistical processes used for predictive analytics such as calculating attribute importance, clustering methods, regression analysis, classification techniques, ensemble models, and neural networks. In these chapters you will also get to understand the automation processes for each of these statistical processes using ODM and ORE along with their application in a real-life business use case.

What you'll learn
Discover the functionality of Oracle Data Miner and Oracle R EnterpriseGain methods to perform in-database predictive analyticsUse Oracle's SQL and PLSQL APIs for building analytical solutionsAcquire knowledge ofcommon and widely-used business statistical analysistechniques
Who this book is for
IT executives, BI architects, Oracle architects and developers, R users and statisticians.

Autorenporträt
Sibanjan is a Sr Analyst for Business Intelligence and Data Science evangelist. He has a strong consulting experience on Business Systems and Data Analytics. As a highly empowered consultant offering around 7 yrs of cross functional experience in the industry, he has helped several organizations to improve, automate and operationalize analytics for their business processes. He comes with a background of implementing business processes using Oracle ERP systems and predictive analytics solutions using Oracle Data Miner and Oracle R Enterprise. He is a Master of Business Analytics from Singapore Management University and holds several certification credentials such as OCA, OCP, ITIL V3, CSCMS and Six Sigma Green belt.