Data Science Using Oracle Data Miner and Oracle R Enterprise

Transform Your Business Systems into an Analytical Powerhouse
1. Auflage 2016
ISBN: 978-1-4842-2362-8
Verlag: Apress

Medienkombination, Englisch, 442 Seiten, eBook, Format (B × H): 155 mm x 235 mm

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 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 business statistical analysis techniques

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

Autoren/Hrsg.

Das, Sibanjan

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Datenbankprogrammierung Oracle
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsinformatik, SAP, IT-Management
• Mathematik | Informatik EDV | Informatik Business Application Mathematische & Statistische Software
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Wirtschaftsinformatik

Inhaltsverzeichnis

Introduction
Chapter 1: Getting Started with Oracle Advanced Analytics

· Overview of Data Science and CRISP-DM Methodology

· Overview of machine learning and its application in industries

· Getting started with Oracle Advanced Analytics- Oracle Data Miner and Oracle R Enterprise

· Analytical SQL and PL/SQL functions

· Summary



Chapter 2: Installation and Hello World



· Oracle Data Miner Installation

· Sample Hello World Oracle Data Miner workflow

· Oracle Data Miner components for SQL Developer GUI

· Oracle R Enterprise Installation

· Sample Hello World program using Oracle R

· Summary

Chapter 3: Clustering Methods
· Approaches for cluster analysis

· K-means algorithm fundamentals

· K-means algorithm in Oracle Advanced Analytics

· Metrics for evaluating clustering algorithms

· Create clusters using Oracle SQL and PLSQL API's

· Create clusters using Oracle R Enterprise

· Create clusters using Oracle SQL Developer GUI

· Case Study - Customer Segmentation

· Summary

Chapter 4: Association Rules

· Introduction to association rules

· Terminologies associated with association rules

· Apriori algorithm fundamentals

· Identify interesting rules

· Association rules using Oracle SQL and PLSQL API's

· Association rules using Oracle R Enterprise

· Association rules using Oracle SQL Developer GUI

· Case Study - Market Basket Analysis

· Summary

Chapter 5: Regression Analysis

· Understanding Relationships

· Introduction to Regression Analysis

· OLS Regression fundamentals

· OLS Regression using Oracle Advanced Analytics

· GLM and Ridge Regression Overview

· GLM Regression using Oracle SQL and PLSQL API's

· GLM Regression using Oracle R Enterprise
· GLM Regression using Oracle SQL Developer GUI

· Case Study - Sales Forecast
· Summary

Chapter 6: Classification Techniques

· Overview of classification techniques

· Logistics Regression fundamentals

· Decision Tree fundamentals

· SVM fundamentals

· Naïve Bayes fundamentals

· Classification using Oracle Advanced Analytics
· Classification using Oracle SQL and PLSQL API's

· Classification using Oracle R Enterprise

· Classification using Oracle SQL Developer GUI

· Case Study - Customer Churn Prediction

· Summary



Chapter 7: Advanced Topics

· Overview of Neural Networks

· Neural Network using Oracle Advanced Analytics

· Overview of Anomaly detection

· Anomaly detection using Oracle Advanced Analytics

· Overview of Random Forest
· Random Forest using Oracle Advanced Analytics

· Chapter 8: Solution Deployment

· Oracle Data Miner Import and Export functionality

· Introduction to PMML



· Generating PMML from Oracle Advanced Analytics models

Über Autor(innen)

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.