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Data Science Using Oracle Data Miner and Oracle R Enterprise: Transform Your Business Systems into an Analytical Powerhouse de Sibanjan Das

Data Science Using Oracle Data Miner and Oracle R Enterprise: Transform Your Business Systems into an Analytical Powerhouse

Autor Sibanjan Das
en Limba Engleză Paperback – 23 dec 2016
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 Enterprise
  • Gain methods to perform in-database predictive analytics
  • Use Oracle's SQL and PLSQL APIs for building analytical solutions
  • Acquire knowledge ofcommon and widely-used business statistical analysis techniques
Who this book is for
IT executives, BI architects, Oracle architects and developers, R users and statisticians.


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Specificații

ISBN-13: 9781484226131
ISBN-10: 1484226135
Pagini: 289
Ilustrații: XXII, 289 p. 318 illus., 289 illus. in color.
Dimensiuni: 155 x 235 x 17 mm
Greutate: 0.45 kg
Ediția:1st ed.
Editura: Apress
Colecția Apress
Locul publicării:Berkeley, CA, United States

Cuprins

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.

Notă biografică

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.

Textul de pe ultima copertă

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.

Caracteristici

A unified architecture and embedded workflow to automate various analytics steps Covers Oracle's Advanced Analytics capabilities using Oracle Data Miner and Oracle R Enterprise Covers Oracle R Enterprise functions and embedded R SQL queries