Predictive Analytics using Oracle Data Mining Ed 1

Duration: 2 Days

What you will learn

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.

Audience

Data Analyst
Data Scientist
Database Administrators

Related Training

Required Prerequisites
No prerequisites equired

Course 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

Course Topics

Introduction

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

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

Understanding the Data Mining Process

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

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

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

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

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 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

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

Mining Structured and Unstructured Data

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

Using Predictive Queries

What are Predictive Queries?
Creating Predictive Queries
Examining Predictive Results

Deploying Predictive models

Requirements for deployment
Deployment Options
Examining Deployment Options