Oracle Database 11g Data Mining Techniques

Oracle Database 11g Data Mining Techniques Training & Placements in Chennai SQL STAR

Oracle Database 11g: Data Mining Techniques
(The Course materials and Course Completion Certificates are directly delivered from Oracle University to those required for Foreign Opportunity)

(Government of India Approved Education Center – SQLSTAR Global)

Learn To:

In this course, students review the basic concepts of data mining and learn how leverage the predictive analytical power of the Oracle Database Data Mining option by using Oracle Data Miner 11g Release 2. The Data Miner GUI provides intuitive tools that help you to 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.

In this course, students review the basic concepts of data mining and learn how leverage the predictive analytical power of the Oracle Database Data Mining option by using Oracle Data Miner 11g Release 2. The Oracle Data Miner GUI is an extension to Oracle SQL Developer 3.0 that enables data analysts to work directly with data inside the database. The Data Miner GUI provides intuitive tools that help you to 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 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.

Suggested Prerequisite

Aworking knowledge of: The SQL language and Oracle Database designand administration

Audience

Application Developers
Database Administrators
BusinessAnalysts
DataWarehouse Analyst

Course Objectives

Explainbasic data mining concepts and describe the benefits of predictiveanalysis
Understandprimary data mining tasks, and describe the key steps of a datamining process
Use the Oracle Data Miner to build,evaluate, and apply multiple datamining models
UseOracle Data Mining's predictions and insights to address manykinds of business problems, including: Predict individualbehavior, Predict values, Find co-occurring events
Learnhow to deploy data mining results for real-time access byend-Users

Course Topics
Introduction

SuggestedCourse Pre-requisites
SuggestedCourse Schedule
Reviewlocation of additional resources (including ODM and SQL Developerdocument ation and online resources)
Practiceand Solutions Structure
ClassSample Schemas
Course Objectives

Overviewing Data Mining Concepts

Whyuse Data Mining?
Examplesof Data Mining Applications
SupervisedVersus Unsupervised Learning
SupportedData Mining Algorithms and Uses
Whatis Data Mining?

Understanding the Data Mining Process

CommonTasks in the Data Mining Process

Introducing Oracle Data Miner 11g Release 2

Datamining with Oracle Database
Introducing the SQL Developer interface
Previewing Data Miner Workflows
Examining Data Miner Nodes
Identifying Data Miner interface components
Setting up Oracle Data Miner
Accessing the Data Miner GUI

Using Classification Models

Adding a Data Source to the Workflow
Examining Class Build Tabs
Creating Classification Models
Comparing the Models
Building the Models
Reviewing Classification Models
Selecting and Examining a Model
Using the Data Source Wizard

Using Regression Models

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

Performing Market Basket Analysis

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

Using Clustering Models

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

Performing Anomaly Detection

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

Deploying Data Mining Results

Requirements for deployment
DeploymentTasks
Examining Deployment Options

Oracle Authorized Test Center

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