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Data Science Using Oracle Data Miner and Oracle R Enterprise, 1st ed. Transform Your Business Systems into an Analytical Powerhouse

Langue : Anglais

Auteur :

Couverture de l’ouvrage Data Science Using Oracle Data Miner and Oracle R Enterprise

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 of common and widely-used business statistical analysis techniques

Who this book is for

IT executives, BI architects, Oracle architects and developers, R users and statisticians.


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 AnalyticsGLM 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
Overview of Predictive Queries
Predictive Queries using Oracle Advanced Analytics
Overview of Product Recommendation Engine
Product Recommendation engine using Oracle Advanced Analytics
Summary
Chapter 8: Solution Deployment

Oracle Data Miner Import and Export functionality
Introduction to PMML
Generating PMML from Oracle Advanced Analytics models

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.

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

Date de parution :

Ouvrage de 289 p.

15.5x23.5 cm

Disponible chez l'éditeur (délai d'approvisionnement : 15 jours).

Prix indicatif 52,74 €

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