Sibanjan Das

Data Science Using Oracle Data Miner and Oracle R Enterprise (eBook, PDF)

Transform Your Business Systems into an Analytical Powerhouse

• Format: PDF

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.

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Produktdetails

• Verlag: Springer-Verlag GmbH
• Seitenzahl: 289
• Erscheinungstermin: 22. Dezember 2016
• Englisch
• ISBN-13: 9781484226148
• Artikelnr.: 53148854

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.

Inhaltsangabe

Introduction

Chapter 1 : Getting Started with Oracle Advanced Analytics.- Chapter 2 : Installation and Hello World.- Chapter 3: Clustering Methods.- Chapter 4: Association Rules.- Chapter 5: Regression Analysis.- Chapter 6: Classification Techniques.- Chapter 7: Advanced Topics.- Chapter 8: Solution Deployment.