D91316 - Predictive Analytics using Oracle Data Mining

This Predictive Analytics using Oracle Data Mining Ed 1 training will review the basic concepts of data mining. Expert Oracle University instructors will teach you how to leverage the predictive analytical power of Oracle Data Mining, a component of the Oracle Advanced Analytics option.

Learn To:

  • Explain basic data mining concepts and describe the benefits of predictive analysis.
  • Understand primary data mining tasks, and describe the key steps of a data mining process.
  • Use the Oracle Data Miner to build, evaluate, apply, and deploy multiple data mining models.
  • Use Oracle Data Mining’s predictions and insights to address many kinds of business problems.
  • Deploy data mining models for end-user access, in batch or real-time, and within applications.

Duration: 2.0 days

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Objectives

Upon completing this course, the learner will be able to meet these overall objectives:
  • Explain basic data mining concepts and describe the benefits of predictive analysis
  • Understand primary data mining tasks, and describe the key steps of a data mining process
  • Use the Oracle Data Miner to build, evaluate, apply, and deploy multiple data mining models
  • Use Oracle Data Mining’s predictions and insights to address many kinds of business problems
  • Deploy data mining models for batch or real-time access by end-users

Content

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

Audience

  • Database Administrators
  • Data Scientist
  • Data Analyst

Prerequisites

N/A

Certification

product-certification

This course is not associated with any Certification.

Course Benefits

product-benefits
  • Career growth
  • Broad Career opportunities
  • Worldwide recognition from leaders
  • Up-to Date technical skills
  • Popular Certification Badges

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