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Oracle Data Mining Training Course

Oracle BI training © 2016 by Burleson Corporation

 

This course is taught at your Company site with up to 20 students. 

Click here for on-site course prices

Optional supplemental mentoring

 

Key Features

* Ensure the success of your Oracle Data Mining project by learning from an experienced Oracle Data Mining expert.

* Get a pragmatic learning experience in Oracle Data Mining

* See industry best practices for Oracle  Data Mining and supplemental  Data Mining  tools.

* Understand the evolution of  Data Mining  needs, from tactical data to hypothesis testing and forecasting.

* See how to use Oracle Data Mining for  trend analysis.

* Learn proven methods for data cleansing.

* See how to collect and analyze terabytes of operational data.

* See how to build Oracle  Data Mining  Interactive Dashboards for executive management.

* Understand the secrets for hypercharging your Oracle Data Mining queries.

* See how to leverage Oracle  Data Mining.

* Learn to use Oracle  Data Mining  answers for sophisticated trend analysis.

  

Course Description

Oracle  Data Mining Training Course is an intense  training course that is designed to give the student maximum exposure to Oracle  Data Mining

Low on theory and long on real-world applications, this Oracle Data Mining training class is taught by an experienced Oracle Data Mining DBA who teaches Oracle Data Mining secrets and tips.

 Every Oracle  Data Mining  shop has different requirements, and this Oracle Data Mining class can also be customized according to your specific needs along with follow-up supplemental mentoring to guarantee your success in your  Oracle Data mining deployment.

BC is proud to be the All-American team with pragmatic training from expert instructors.

Book Required


Oracle Data Mining
Mining Gold from your Warehouse


Dr. Carolyn K. Hamm 


ISBN = 978-0974448633

Audience

This Oracle  Data Mining course is designed for practicing Oracle professionals who have basic experience with Oracle.

Prior experience with Oracle is not required, but experience using Oracle database is highly desirable.

This course is intended for anyone involved in the Oracle Data Mining project, including IT managers, data analysts, developers, systems analysts and end-user liaisons.

Curriculum Design

This Oracle  Data Mining course was designed by Donald K. Burleson, an acknowledged leader in Oracle database administration.  Mr. Burleson has over two decades of real-world Data Mining experience, and strategic data analysis.  In this intensive Oracle Data Mining class, Mr. Burleson shares hidden secrets for maximizing your experience with Oracle  Data Mining.

Learning Objectives

By the end of this Oracle  Data Mining training course the student will understand the Oracle Data Mining infrastructure, Data Mining planning, data warehouse data collection.  In addition, the student will learn to use Oracle  Data Mining best practices.



Oracle  Data Mining  (Oracle Data Mining)
Training Class Syllabus

An intensive 3-day to 5-day customized Oracle Data Mining course
Course topics are customized to your specific Data Mining project needs

Introduction to Oracle Data Mining.

Data Mining and Transactional Applications.
Daily Data Mining.
Data Mining Balanced Scorecard.
Enterprise Planning and Budgeting.
Activity-Based Management for Data Mining.
Oracle Integration Components Enabling Data Mining.
Data Mining Data Hubs.
Business Activity Monitoring.
BPEL Process Manager.
Enterprise Messaging Service.
Custom Data Warehouse Solutions.
The Role of the Oracle Database in Data Mining .
Oracle Warehouse Builder.
Oracle Data Mining Standard Edition.
Oracle Data Mining Enterprise Edition.
Data Mining  (XML) Publisher.
Oracle Portal.
Spreadsheet Add-ins.
Building Custom Data Mining Applications.
Emerging Trends in Data Mining.

Oracle?s Transactional Data Mining.

Transactional Data Mining.
Oracle?s Daily Data Mining.
How Data Mining  Works.
Varieties of Data Mining .
Data Mining Balanced Scorecards.
Oracle Balanced Scorecard Structure.
OBSC Architecture.
Creating an Oracle Balanced Scorecard.
Data Mining Data Hubs.
The Oracle Customer Data Hub.
Internals of Oracle Data Hubs.
Other Oracle Data Hubs.
Transactional vs. Strategic Data Mining.

Introduction to Oracle Data Warehousing

Oracle Data Warehousing Basics.
Oracle Database Analysis

Data Mining
Schema Considerations.
Managing an Oracle-based Data Warehouse.
Oracle/PeopleSoft EPM.
Oracle/Siebel Business Analytics Applications.
Build or Buy?  Choosing a Custom Data Mining solution.


 Data Mining  project Planning.

Uncovering Key Business information Initiatives.
Information Sources for Data Mining.
What is Important in Data Mining?
Data Mining Accountability.
Securing Business Sponsorship.
Establish a Steering Committee.
The Data Mining Project Review Board.
Endorsing a Methodology.
Choosing a of Data Mining Methodology. 
Staffing the Data Mining Project.
Data Mining Organization Structure.
Maximizing the End-User Experience.
Engaging the Business: Education and Training.
Managing Risk.
Managing Data Mining Expectations.
Data Mining Contingency Allocation.
Financial and Technology Risk Assessment.
Data Mining Feasibility Analysis

Understanding Data Mining Needs.

Avoiding Bad Deployment Choices.
Creating Independent Data Marts.
Building for Flexible Reporting.
Identifying Data Mining Sources of Information
Limiting and scrubbing Internal Data.
Ensuring Current High-Quality Data.
Planning for Data Mining Growth & Flexibility.
Project Drivers and Business Types.
Data Mining in Financial Companies.
Data Mining  in Healthcare.
Data Mining in Manufacturing.
Data Mining in Media and Entertainment.
Data Mining in Retail.
Telecommunications.
Other Business Types: Transportation and Utilities.
Data Mining in Educational Institutions.
Government Agencies.
Developing Scope and Gaining Business Support.

 
Justifying  Data Mining  Projects - cost/benefit analysis

Data Mining conceptual planning.
Evaluating Business Constraints.
Where to Start Justification.
Measuring Value in Data Mining.
Common Metrics to Measure.
Common Budgeting Techniques.
Total Cost of Ownership.
Modeling Total Cost of Ownership.
Return on Investment.
Modeling Return on Investment.
Claiming Success.
Choosing a Platform for Oracle Data Mining.

Scaling Up Platforms Versus Scaling Out.
Hardware Platforms for Data Mining.
Cost and Availability Considerations.
Data Mining Manageability Considerations.
Sizing the Data Mining Hardware Platform.
Information Needed for Warehouse Hardware Sizing.
Benchmarking a Data Mining system.
Sizing Hardware for Data Mining Tools.

Designing Oracle Data Mining for Maximum Usability.

Approaches for Data Mining Design.
Key Data Mining Design Considerations.
Features for Design ? Enhancing Performance.
Business Scenario.
Normalized Database Design for Data Mining.
Multi-Dimensional Database Design.
Online Analytical Processing (OLAP) Design.
Selecting the Best Design Approach for your Data Mining Project.

Oracle Data Mining Tools.

Oracle Portal and Portal Products.
Using Oracle Portal.
Building and Deploying Oracle Portal and Portlets.
Data Mining and XML Publisher.
Oracle Reports and Data Mining.
Oracle  Data Mining  Reporting Workbench (Actuate).
Ad hoc Query and Analysis for Data Mining.
Discoverer and Data Mining Standard Edition.
Building Data Mining Applications.
JDeveloper and  Data Mining  Beans.
Using Oracle Data Miner (ODM). 

Oracle Data Loading and ETL.
 
Oracle Database Data Loading Features.
Embedded ETL in the Oracle Database.
SQL*Loader.
Change Data Capture (CDC).
Transportable Tablespaces.
Data Pump.
Oracle Warehouse Builder and Data Mining.
OWB Packaging.
Typical Steps when using OWB.
ETL Design in OWB.
OWB and Dimensional Models.
The OWB Process Editor.
Balancing Data Loading Choices.

Managing the Oracle Data Warehouse.

Oracle Enterprise Manager Grid Control.
Database Performance Monitoring.
Database Administration.
Database Maintenance.
Database Topology.
Management and Management Options.

Data Mining  Performance Tuning.

Understanding Performance Challenges in Data Mining Applications.
Causes of Poor Data Mining Performance.
Successful Approaches to Performance Tuning.
Critical Tasks for Performance Tuning Lifecycle.
Hardware Configuration for Data Mining.
Software Configuration for Data Mining.
Database Application Design.
Business Scenario: Tuning Our Sample Solution.
Oracle Enterprise Manager Advisory Framework.
Oracle Data Mining Best Practices.
 

Introduction to Model Building  

What is Data Mining?  

Components of Oracle Data Miner  

Sampling Data from the Database  

Concentrating on a customer  

Building a Classification Model  

Naming Data Mining Activities  

Running a Data Mining Activity  

Viewing your Results  

The ODM ROC Curve  

Applying changes to a Model  

Attribute Importance in the Na?e Bayes Model  

Building Na?e Bayes Model with Fewer Attributes  

Applying the Model  

Using the Create View Wizard  

Scoring New Data  

Viewing Top Rankings  

Conclusion  

 

Adaptive Bayes Network and Decision Trees  

Introduction to Classification  

Data Mining Classification Models  

Using the Models  

Importing a Dataset  

Exploring and Reducing the Dataset  

Viewing Attribute Histograms  

Attribute Importance  

Comparing Na?e Bayes Models for Forest Cover  

Adaptive Bayes Single Feature Model  

Building the Adaptive Bayes Network Model  

Sampling  

Viewing Adaptive Bayes Network Results  

Interpreting Adaptive Bayes Network Results  

Building the Adaptive Bayes Multi Feature Model  

Using the ROC Feature  

Introducing Cost Bias to the Classification Model  

Building a Decision Tree  

The Decision Tree Classification Model  

Decision Tree Classification Rules  

Conclusion  

 

Using Support Vector Machines  

Introduction to Support Vector Machine  

Inside Support Vector Machines  

Importing the Irish Wind Data File  

Computing a New Attribute with Compute Field Transformation Wizard  

Building the SVM Model  

Handling Outlier Values in SVM Analysis  

Missing Values in SVM Analysis  

Sparse Data in SVM Analysis  

Normalization of SVM Data  

Linear and Gaussian Kernels  

SVM and Over-fitting  

SVM Results with Gaussian Kernel  

Importing Boston House Price Data  

Building SVM Classification Models  

Interpreting the SVM Results  

Refining the SVM Model  

Building a SVM Regression Model  

Regression Model Results  

Linear Regression Analysis  

Drilling into the SVM Data  

Using Text Data in SVM Predictive Models  

Importing CLOB Data  

Loading CLOB Data into the Oracle Database  

Building a SVM Text Model  

Interpreting the SVM text Data  

Conclusion  

Creating Clusters and Cohorts  

Clustering and Cohorts  

The k-Means Cluster  

Using O-Cluster  

O-Cluster Sensitivity Settings  

Using K-Means for Clustering  

Examining the CoIL Data  

Building a K-Means Cluster  

Finding majority cohort values  

Comparing data sub-sets with K-Means  

Choosing the Appropriate Data Mining Algorithm  

When to use K-Means Analysis  

When to use O-Cluster Analysis  

Applying the Cluster  

Publishing the Cluster Results  

Publishing to a File  

Using the Discoverer Gateway for Publication  

Publishing to an Oracle Database  

Importing the model to a different Oracle database  

Conclusion  

 

Inside Oracle Data Miner  

Exploring Data Miner  

Data Miner Activity Builder Tasks  

Quantile Binning  

Using the Discretize Transform Wizard  

Customizing Discretize Transformations  

Using the Aggregate Transformation Wizard  

Recode Transformation Wizard  

Using the Split Transformation Wizard  

Using the Stratified Sample Transformation Wizard  

Using the Filter Single-Record Transformation Wizard  

Inside the Sample Transformation Wizard  

Preparing datasets for Data Mining Activities  

Using the Missing Values Transformation Wizard  

Using the Normalize Transformation Wizard  

Using the Numeric Transformation Wizard  

Using the Outlier Treatment Transformation Wizard  

Conclusion  

 

Predictive Analytics  

Predictive Analytics in Data Mining  

Explain Procedure  

Predict Procedure  

Explain Wizard  

Predict Wizard  

Applying Predictive Analytics  

Conclusion  

 

Personalized Form Letter Generation with Oracle BI Publisher  

Scenarios for using ODM with BI Publisher  

Building a Decision Tree Model  

Results of the Decision Tree Model  

Scoring the Apply Dataset.  

Using SQL to View Results of Scored Data  

Creating a Report using BI Publisher Enterprise Server  

Using Template Builder for Oracle BI Publisher  

Adding Fields to the Word Template using BI Publisher Template Builder  

Creating a Personalized Customer Letter with Three Offers  

Scenario for Personalizing a Form Letter  

Building a Decision Tree Model using Oracle Data Miner  

Accuracy of the Fund Raiser DT Model  

Results of the Fund Raiser DT Model  

Generating XML Data using BI Publisher  

Creating a Form Letter with the Template Builder  

Conclusion  

Book Conclusion  

 

Installing Oracle Data Miner  

ODM Tutorial  

Purpose  

Time to Complete  

Topics  

Overview  

Prerequisites  

Enabling the DMSYS Account  

Creating and Configuring A Data Mining Account  

Installing Oracle Data Miner  

Summary  

 

To view all of our Oracle  Data Mining training courses please visit our main training page here.

   

Burleson is the American Team

Note: This Oracle documentation was created as a support and Oracle training reference for use by our DBA performance tuning consulting professionals.  Feel free to ask questions on our Oracle forum.

Verify experience! Anyone considering using the services of an Oracle support expert should independently investigate their credentials and experience, and not rely on advertisements and self-proclaimed expertise. All legitimate Oracle experts publish their Oracle qualifications.

Errata?  Oracle technology is changing and we strive to update our BC Oracle support information.  If you find an error or have a suggestion for improving our content, we would appreciate your feedback.  Just  e-mail:  and include the URL for the page.


                    









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