Oracle Database 11g Data Mining Techniques 1.0 Training & Placements in Chennai ISQL Global
Oracle Database
11g: Data Mining Techniques
(The Course Materials and Course Completion Certificates are directly delivered from Oracle University to those seeking for Foreign
Opportunity)
(Government of India Approved Education Center - ISQL 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,
securityis maximized and information latencyis minimized.
Suggested Prerequisite- Aworking knowledge of: The SQL language and Oracle Database designand administration
Audience- Application Developers
- Database Administrators
- Business Analysts
- Data Warehouse 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
- Use Oracle 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 Developerdocumentation 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
- Settingup 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
- ReviewingRegression Models
- Comparing the Models
- CreatingRegression Models
Performing Market Basket Analysis- Creating an Association Rules Model
- Creating a New Workflow
- ReviewingAssociation Rules
- Whatis Market Basket Analysis?
- Adding a Data Source to th Workflow
- Building the Model
- DefiningAssociation Rules
- Examining Test Results
Using Clustering Models- ComparingModel Results
- Defining and Building Clustering Models
- Adding Data Sources to the Workflow
- DefiningOutput Format
- DescribingAlgorithms 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
- EvaluatingResults
- Applying the Model
Deploying Data Mining Results- Requirementsfor deployment
- DeploymentTasks
- Examining Deployment Options