Oracle - Course Details

OraDDMT11g Oracle Database 11g: Data Mining Techniques

Oracle Course Description:
In this course, students review the basic concepts of data mining and learn how leverage the predictive analytical power of the Oracle Database Data Mining option by using Oracle Data Miner 11g Release 2. The Oracle Data Miner GUI is an extension to Oracle SQL Developer 3.0 that enables data analysts to work directly with data inside the database.
Oracle Course Duration:
2 Days Training
Oracle Course Target Audience:
Application Developers,Business Analysts,Data Warehouse Analyst,Database Administrators
Oracle Course Prerequisite:
A working knowledge of: The SQL language and Oracle Database design and administration
Course Content:
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