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

Das, Sibanjan

Softcover

ISBN 10: 1484226135 ISBN 13: 9781484226131

Publisher: Apress, 2016

Synopsis

Automate the predictive analytics process using Oracle Advanced Analytics. This book talks about how Oracle Data Miner and Oracle R Enterprise which are the two components of Oracle Advanced Analytics 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 Using Oracle Data Miner and Oracle R Enterprise starts with an introduction to Data Science, covering why automation is necessary and the level of complexity in various Data Science tasks. 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 and machine learning techniques used for predictive analytics such as clustering methods, regression analysis, classification techniques, calculating attribute importance, 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 Will 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 of common and widely-used machine learning techniques in business

Who This Book Is For
* IT executives planning for quick ROI from business analytics solutions
* BI architects responsible for business analytics implementations
* Oracle architects and developers aspiring to learn data science and analytics
* R users and statisticians wanting to leverage the capability of R in Oracle Database

About this title

About the Author

Sibanjan Das is a Business Analytics and Data Science consultant. He has over six years of experience in IT industry working on ERP systems, implementing predictive analytics solutions in business systems and Internet of Things. An enthusiastic and passionate professional about technology & innovation, he has the passion for wrangling with data from early days of his career. He also enjoys reading, writing, and networking. His writings have appeared in various Analytics Magazines, and Klout has rated him among the top 2% professionals in the world talking about Artificial Intelligence, Machine Learning, Data Science and Internet of Things.

Sibanjan holds a Master of IT degree with a major in Business Analytics from Singapore Management University, Singapore and is a Computer Science Engineering graduate from Institute of Technical Education and Research, India. He is a Six Sigma Green Belt from Institute Of Industrial Engineers and also holds several industry certifications such as OCA, OCP, CSCMS, and ITIL V3.

From the Back Cover

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.

Edition details

• Publisher: Apress
• Publication date: 2016
• Language: English
• ISBN 10: 1484226135
• ISBN 13: 9781484226131
• Binding: Paperback
• Edition number: 1
• Number of pages: 311