Course Specifics

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

Thinking about Onsite?

If you need training for 3 or more people, you should ask us about onsite training. Putting aside the obvious location benefit, content can be customised to better meet your business objectives and more can be covered than in a public classroom. It's a cost effective option.

Submit an enquiry from any page on this site, and let us know you are interested in the requirements box, or simply mention it when we contact you.

Scheduled Dates

  • Location
    Cost
    Duration
    Start Date
     
Virtual Class
  • Virtual Class
    990
    2
    29 Dec 14
  • Virtual Class
    1230
    2
    29 Dec 14

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