D91316 – Predictive Analytics using Oracle Data Mining

  • Duration: 10 weeks
Categories:

Module 1: Introduction

  • Course Objectives
  • Suggested Course Prerequisites
  • Suggested Course Schedule
  • Class Sample Schemas
  • Practice and Solutions Structure
  • Review location of additional resources

Module 2: Predictive Analytics and Data Mining Concepts

  • What is the Predictive Analytics
  • Introducting the Oracle Advanced Analytics (OAA) Option
  • What is Data Mining
  • Why use Data Mining
  • Examples of Data Mining Applications
  • Supervised Versus Unsupervised Learning
  • Supported Data Mining Algorithms and Uses

Module 3: Understanding the Data Mining Process

  • Common Tasks in the Data Mining Process
  • Introducing the SQL Developer interface

Module 4: Introducing Oracle Data Miner 4.1

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

Module 5: Using Classification Models

  • Reviewing Classification Models
  • Adding a Data Source to the Workflow
  • Using the Data Source Wizard
  • Using Explore and Graph Nodes
  • Using the Column Filter Node
  • Creating Classification Models
  • Building the Models
  • Examining Class Build Tabs

Module 6: 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

Module 7: 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

Module 8: Performing Market Basket Analysis

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

Module 9: 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

Module 10: Mining Structured and Unstructured Data

  • Dealing with Transactional Data
  • Handling Aggregated (Nested) Data
  • Joining and Filtering data
  • Enabling mining of Text
  • Examining Predictive Results

Module 11: Using Predictive Queries

  • What are Predictive Queries?
  • Creating Predictive Queries
  • Examining Predictive Results

Module 12: Deploying Predictive models

  • Requirements for deployment
  • Deployment Options
  • Examining Deployment Options

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