Course Outline

Course Outline

Introduction

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

Overviewing Data Mining Concepts

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

Understanding the Data Mining Process

  • Common Tasks in the Data Mining Process

Introducing Oracle Data Miner 11g Release 2

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

Using Classification Models

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

Using Regression Models

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

Performing Market Basket Analysis

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

Using Clustering Models

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

Performing Anomaly Detection

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

Deploying Data Mining Results

  • Requirements for deployment
  • Deployment Tasks
  • Examining Deployment Options

Scheduled Dates

  • Location
    Cost
    Duration
    Date
Request Availability OR Enquire by clicking a dateVirtual Class
  • Virtual Class
    990
    2
  • Virtual Class
    1230
    2
  • TBA
    0
    2
  • London (Central)
    • TBA
      0
      2
    • Manchester (Greater)
      • TBA
        0
        2
      • Berkshire
        • TBA
          0
          2
        • Midlands (West)
          • TBA
            0
            2
          • Ask
            • Ask
              0
              2
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