Predictive Analytics using Oracle Data Mining

Course Fees: $2684.00 excl. GST
Printed Manual: $0.00 excl. GST
Course Duration: 2 days
Course Manual

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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.

Benefits to You

When you've completed this course, you'll be able to use the Oracle Data Miner 4.1, the Oracle Data Mining workflow GUI, which enables data analysts to work directly with data inside the database. The Data Miner GUI provides intuitive tools that help you explore the data graphically, build and evaluate multiple data mining models, apply Oracle Data Mining models to new data, and deploy Oracle Data Mining's predictions and insights throughout the enterprise.

Oracle Data Miner's SQL APIs - Get Results in Real-Time

Oracle Data Miner's SQL APIs automatically mine Oracle data and deploy results in real-time. Because the data, models, and results remain in the Oracle Database, data movement is eliminated, security is maximized and information latency is minimized.

  • Database Administrators
  • Data Scientist
  • Data Analyst
  • 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

Introduction

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

Predictive Analytics and Data Mining Concepts

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

Understanding the Data Mining Process

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

Introducing Oracle Data Miner 4.1

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

Using Classification Models

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

Using Regression Models

  • Using the Data Source Wizard
  • Selecting a Model
  • Building the Models
  • Comparing the Models
  • Creating Regression Models
  • Reviewing Regression Models
  • Performing Data Transformations
  • Adding a Data Source to the Workflow

Using Clustering Models

  • Adding Data Sources to the Workflow
  • Comparing Model Results
  • Exploring Data for Patterns
  • Describing Algorithms used for Clustering Models
  • Defining and Building Clustering Models
  • Defining Output Format
  • Selecting and Applying a Model
  • Examining Cluster Results

Performing Market Basket Analysis

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

Performing Anomaly Detection

  • Applying the Model
  • Adding Data Sources to the Workflow
  • Evaluating Results
  • Building the Model
  • Examining Test Results
  • Reviewing the Model and Algorithm used for Anomaly Detection
  • Creating the Model

Mining Structured and Unstructured Data

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

Using Predictive Queries

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

Deploying Predictive models

  • Examining Deployment Options
  • Requirements for deployment
  • Deployment Options