Module 1: Introduction

• Suggested Course Pre-requisites
• Suggested Course Schedule
• Review location of additional resources (including ODM and SQL Developer documentation and online resources)
• Practice and Solutions Structure
• Class Sample Schemas
• Course Objectives

Module 2: Overviewing Data Mining Concepts

• Why use Data Mining?
• Examples of Data Mining Applications
• Supervised Versus Unsupervised Learning
• Supported Data Mining Algorithms and Uses
• What is Data Mining?

Module 3: Understanding the Data Mining Process

• Common Tasks in the Data Mining Process

Module 4: Introducing Oracle Data Miner 11g Release 2

• Data mining with Oracle Database
• Introducing the SQL Developer interface
• Previewing Data Miner Workflows
• Examining Data Miner Nodes
• Identifying Data Miner interface components
• Setting up Oracle Data Miner
• Accessing the Data Miner GUI

Module 5: Using Classification Models

• Adding a Data Source to the Workflow
• Examining Class Build Tabs
• Creating Classification Models
• Comparing the Models
• Building the Models
• Reviewing Classification Models
• Selecting and Examining a Model
• Using the Data Source Wizard

Module 6: Using Regression Models

• Building the Models
• Performing Data Transformations
• Using the Data Source Wizard
• Adding a Data Source to the Workflow
• Selecting a Model
• Reviewing Regression Models
• Comparing the Models
• Creating Regression Models

Module 7: Performing Market Basket Analysis

• Creating an Association Rules Model
• Creating a New Workflow
• Reviewing Association Rules
• What is Market Basket Analysis?
• Adding a Data Source to th Workflow
• Building the Model
• Defining Association Rules
• Examining Test Results

Module 8: Using Clustering Models

• Comparing Model Results
• Defining and Building Clustering Models
• Adding Data Sources to the Workflow
• Defining Output Format
• Describing Algorithms used for Clustering Models
• Examining Cluster Results
• Selecting and Applying a Model
• Exploring Data for Patterns

Module 9: Performing Anomaly Detection

• Building the Model
• Creating the Model
• Reviewing the Model and Algorithm used for Anomaly Detection
• Adding Data Sources to the Workflow
• Examining Test Results
• Evaluating Results
• Applying the Model

Module 10: Deploying Data Mining Results

• Requirements for deployment
• Deployment Tasks
• Examining Deployment Options