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Data Mining  Online Training

Data Mining


The main aim of data mining is to extract knowledge, or information, which is stored in very large databases. This course covers many of the concepts that are fundamental to understanding and successfully applying data mining methods. Statistical concepts are discussed without mathematically complex formulation. Practical sessions will use the latest versions of standard software rather than data mining – the emphasis of the course is on techniques rather than data mining software
  • A working knowledge of: The SQL language and Oracle Database design and administration
  • It is a 12 days program and extends up to 2hrs each.
  • The format is 40% theory, 60% Hands-on.

  • It is a 3 days program and extends up to 8hrs each.
  • The format is 40% theory, 60% Hands-on.
    Private Classroom arranged on request and minimum attendies for batch is 4.
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
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