Visão Geral

O Curso Oracle  Data Mining  Course é um curso de treinamento intenso projetado para dar ao aluno o máximo de exposição ao Oracle Data Mining Com pouca teoria e longa em aplicações do mundo real, esta aula de treinamento de Oracle Data Mining é ministrada por um experiente DBA de Oracle Data Mining que ensina segredos e dicas de Oracle Data Mining.

Cada loja de Oracle Data Mining tem requisitos diferentes, e essa classe de Oracle Data Mining também pode ser personalizada de acordo com suas necessidades específicas, juntamente com orientação complementar de acompanhamento para garantir seu sucesso em sua implantação de mineração de dados Oracle.

Objetivo

Ao final deste Curso Oracle  Data Mining , o aluno compreenderá a infraestrutura Oracle Data Mining, planejamento de Data Mining, coleta de dados de data warehouse. Além disso, o aluno aprenderá a usar as melhores práticas do Oracle Data Mining

Publico Alvo

• Este curso Oracle Data Mining foi desenvolvido para profissionais Oracle que têm experiência básica com Oracle.
• Experiência anterior com Oracle não é necessária, mas experiência com banco de dados Oracle é altamente desejável.
• Este curso é destinado a qualquer pessoa envolvida no projeto Oracle Data Mining, incluindo gerentes de TI, analistas de dados, desenvolvedores, analistas de sistemas e contatos de usuários finais.

Materiais

Conteúdo Programatico

Introduction to Oracle Data Mining.

1. Data Mining and Transactional Applications.
2. Daily Data Mining.
3. Data Mining Balanced Scorecard.
4. Enterprise Planning and Budgeting.
5. Activity-Based Management for Data Mining.
6. Oracle Integration Components Enabling Data Mining.
7. Data Mining Data Hubs.
8. Business Activity Monitoring.
9. BPEL Process Manager.
10. Enterprise Messaging Service.
11. Custom Data Warehouse Solutions.
12. The Role of the Oracle Database in Data Mining .
13. Oracle Warehouse Builder.
14. Oracle Data Mining Standard Edition.
15. Oracle Data Mining Enterprise Edition.
16. Data Mining  (XML) Publisher.
17. Oracle Portal.
18. Spreadsheet Add-ins.
19. Building Custom Data Mining Applications.
20. Emerging Trends in Data Mining.

Oracle?s Transactional Data Mining.

1. Transactional Data Mining.
2. Oracle?s Daily Data Mining.
3. How Data Mining  Works.
4. Varieties of Data Mining .
5. Data Mining Balanced Scorecards.
6. Oracle Balanced Scorecard Structure.
7. OBSC Architecture.
8. Creating an Oracle Balanced Scorecard.
9. Data Mining Data Hubs.
10. The Oracle Customer Data Hub.
11. Internals of Oracle Data Hubs.
12. Other Oracle Data Hubs.
13. Transactional vs. Strategic Data Mining.

Introduction to Oracle Data Warehousing

1. Oracle Data Warehousing Basics.
2. Oracle Database Analysis
3. Data Mining Schema Considerations.
4. Managing an Oracle-based Data Warehouse.
5. Oracle/PeopleSoft EPM.
6. Oracle/Siebel Business Analytics Applications.
7. Build or Buy?  Choosing a Custom Data Mining solution.

Data Mining  project Planning.

1. Uncovering Key Business information Initiatives.
2. Information Sources for Data Mining.
3. What is Important in Data Mining?
4. Data Mining Accountability.
5. Securing Business Sponsorship.
6. Establish a Steering Committee.
7. The Data Mining Project Review Board.
8. Endorsing a Methodology.
9. Choosing a of Data Mining Methodology. 
10. Staffing the Data Mining Project.
11. Data Mining Organization Structure.
12. Maximizing the End-User Experience.
13. Engaging the Business: Education and Training.
14. Managing Risk.
15. Managing Data Mining Expectations.
16. Data Mining Contingency Allocation.
17. Financial and Technology Risk Assessment.
18. Data Mining Feasibility Analysis

Understanding Data Mining Needs.

1. Avoiding Bad Deployment Choices.
2. Creating Independent Data Marts.
3. Building for Flexible Reporting.
4. Identifying Data Mining Sources of Information
5. Limiting and scrubbing Internal Data.
6. Ensuring Current High-Quality Data.
7. Planning for Data Mining Growth & Flexibility.
8. Project Drivers and Business Types.
9. Data Mining in Financial Companies.
10. Data Mining  in Healthcare.
11. Data Mining in Manufacturing.
12. Data Mining in Media and Entertainment.
13. Data Mining in Retail.
14. Telecommunications.
15. Other Business Types: Transportation and Utilities.
16. Data Mining in Educational Institutions.
17. Government Agencies.
18. Developing Scope and Gaining Business Support.

Introduction to Model Building  

1. What is Data Mining?  
2. Components of Oracle Data Miner  
3. Sampling Data from the Database  
4. Concentrating on a customer  
5. Building a Classification Model  
6. Naming Data Mining Activities  
7. Running a Data Mining Activity  
8. Viewing your Results  
9. The ODM ROC Curve  
10. Applying changes to a Model  
11. Attribute Importance in the Na?e Bayes Model  
12. Building Na?e Bayes Model with Fewer Attributes  
13. Applying the Model  
14. Using the Create View Wizard  
15. Scoring New Data  
16. Viewing Top Rankings  
17. Conclusion  

Adaptive Bayes Network and Decision Trees  

1. Introduction to Classification  
2. Data Mining Classification Models  
3. Using the Models  
4. Importing a Dataset  
5. Exploring and Reducing the Dataset  
6. Viewing Attribute Histograms  
7. Attribute Importance  
8. Comparing Na?e Bayes Models for Forest Cover  
9. Adaptive Bayes Single Feature Model  
10. Building the Adaptive Bayes Network Model  
11. Sampling  
12. Viewing Adaptive Bayes Network Results  
13. Interpreting Adaptive Bayes Network Results  
14. Building the Adaptive Bayes Multi Feature Model  
15. Using the ROC Feature  
16. Introducing Cost Bias to the Classification Model  
17. Building a Decision Tree  
18. The Decision Tree Classification Model  
19. Decision Tree Classification Rules  
20. Conclusion  

Using Support Vector Machines  

1. Introduction to Support Vector Machine  
2. Inside Support Vector Machines  
3. Importing the Irish Wind Data File  
4. Computing a New Attribute with Compute Field Transformation Wizard  
5. Building the SVM Model  
6. Handling Outlier Values in SVM Analysis  
7. Missing Values in SVM Analysis  
8. Sparse Data in SVM Analysis  
9. Normalization of SVM Data  
10. Linear and Gaussian Kernels  
11. SVM and Over-fitting  
12. SVM Results with Gaussian Kernel  
13. Importing Boston House Price Data  
14. Building SVM Classification Models  
15. Interpreting the SVM Results  
16. Refining the SVM Model  
17. Building a SVM Regression Model  
18. Regression Model Results  
19. Linear Regression Analysis  
20. Drilling into the SVM Data  
21. Using Text Data in SVM Predictive Models  
22. Importing CLOB Data  
23. Loading CLOB Data into the Oracle Database  
24. Building a SVM Text Model  
25. Interpreting the SVM text Data  
26. Conclusion  

Justifying  Data Mining  Projects - cost/benefit analysis

1. Data Mining conceptual planning.
2. Evaluating Business Constraints.
3. Where to Start Justification.
4. Measuring Value in Data Mining.
5. Common Metrics to Measure.
6. Common Budgeting Techniques.
7. Total Cost of Ownership.
8. Modeling Total Cost of Ownership.
9. Return on Investment.
10. Modeling Return on Investment.
11. Claiming Success.

Choosing a Platform for Oracle Data Mining.

1. Scaling Up Platforms Versus Scaling Out.
2. Hardware Platforms for Data Mining.
3. Cost and Availability Considerations.
4. Data Mining Manageability Considerations.
5. Sizing the Data Mining Hardware Platform.
6. Information Needed for Warehouse Hardware Sizing.
7. Benchmarking a Data Mining system.
8. Sizing Hardware for Data Mining Tools.

Designing Oracle Data Mining for Maximum Usability.

1. Approaches for Data Mining Design.
2. Key Data Mining Design Considerations.
3. Features for Design ? Enhancing Performance.
4. Business Scenario.
5. Normalized Database Design for Data Mining.
6. Multi-Dimensional Database Design.
7. Online Analytical Processing (OLAP) Design.
8. Selecting the Best Design Approach for your Data Mining Project.

Oracle Data Mining Tools.

1. Oracle Portal and Portal Products.
2. Using Oracle Portal.
3. Building and Deploying Oracle Portal and Portlets.
4. Data Mining and XML Publisher.
5. Oracle Reports and Data Mining.
6. Oracle  Data Mining  Reporting Workbench (Actuate).
7. Ad hoc Query and Analysis for Data Mining.
8. Discoverer and Data Mining Standard Edition.
9. Building Data Mining Applications.
10. JDeveloper and  Data Mining  Beans.
11. Using Oracle Data Miner (ODM). 

Oracle Data Loading and ETL.

1.  Oracle Database Data Loading Features.
2. Embedded ETL in the Oracle Database.
3. SQL*Loader.
4. Change Data Capture (CDC).
5. Transportable Tablespaces.
6. Data Pump.
7. Oracle Warehouse Builder and Data Mining.
8. OWB Packaging.
9. Typical Steps when using OWB.
10. ETL Design in OWB.
11. OWB and Dimensional Models.
12. The OWB Process Editor.
13. Balancing Data Loading Choices.

Managing the Oracle Data Warehouse.

1. Oracle Enterprise Manager Grid Control.
2. Database Performance Monitoring.
3. Database Administration.
4. Database Maintenance.
5. Database Topology.
6. Management and Management Options.

Data Mining  Performance Tuning.

1. Understanding Performance Challenges in Data Mining Applications.
2. Causes of Poor Data Mining Performance.
3. Successful Approaches to Performance Tuning.
4. Critical Tasks for Performance Tuning Lifecycle.
5. Hardware Configuration for Data Mining.
6. Software Configuration for Data Mining.
7. Database Application Design.
8. Business Scenario: Tuning Our Sample Solution.
9. Oracle Enterprise Manager Advisory Framework.
10. Oracle Data Mining Best Practices.

Creating Clusters and Cohorts  

1. Clustering and Cohorts  
2. The k-Means Cluster  
3. Using O-Cluster  
4. O-Cluster Sensitivity Settings  
5. Using K-Means for Clustering  
6. Examining the CoIL Data  
7. Building a K-Means Cluster  
8. Finding majority cohort values  
9. Comparing data sub-sets with K-Means  
10. Choosing the Appropriate Data Mining Algorithm  
11. When to use K-Means Analysis  
12. When to use O-Cluster Analysis  
13. Applying the Cluster  
14. Publishing the Cluster Results  
15. Publishing to a File  
16. Using the Discoverer Gateway for Publication  
17. Publishing to an Oracle Database  
18. Importing the model to a different Oracle database  
19. Conclusion  

Inside Oracle Data Miner  

1. Exploring Data Miner  
2. Data Miner Activity Builder Tasks  
3. Quantile Binning  
4. Using the Discretize Transform Wizard  
5. Customizing Discretize Transformations  
6. Using the Aggregate Transformation Wizard  
7. Recode Transformation Wizard  
8. Using the Split Transformation Wizard  
9. Using the Stratified Sample Transformation Wizard  
10. Using the Filter Single-Record Transformation Wizard  
11. Inside the Sample Transformation Wizard  
12. Preparing datasets for Data Mining Activities  
13. Using the Missing Values Transformation Wizard  
14. Using the Normalize Transformation Wizard  
15. Using the Numeric Transformation Wizard  
16. Using the Outlier Treatment Transformation Wizard

Predictive Analytics  

1. Predictive Analytics in Data Mining  
2. Explain Procedure  
3. Predict Procedure  
4. Explain Wizard  
5. Predict Wizard  
6. Applying Predictive Analytics  
7. Conclusion  

Personalized Form Letter Generation with Oracle BI Publisher  

1. Scenarios for using ODM with BI Publisher  
2. Building a Decision Tree Model  
3. Results of the Decision Tree Model  
4. Scoring the Apply Dataset.  
5. Using SQL to View Results of Scored Data  
6. Creating a Report using BI Publisher Enterprise Server  
7. Using Template Builder for Oracle BI Publisher  
8. Adding Fields to the Word Template using BI Publisher Template Builder  
9. Creating a Personalized Customer Letter with Three Offers  
10. Scenario for Personalizing a Form Letter  
11. Building a Decision Tree Model using Oracle Data Miner  
12. Accuracy of the Fund Raiser DT Model  
13. Results of the Fund Raiser DT Model  
14. Generating XML Data using BI Publisher  
15. Creating a Form Letter with the Template Builder  
16. Conclusion  
17. Book Conclusion  

Installing Oracle Data Miner  

1. ODM Tutorial  
2. Purpose  
3. Time to Complete  
4. Topics  
5. Overview  
6. Prerequisites  
7. Enabling the DMSYS Account  
8. Creating and Configuring A Data Mining Account  
9. Installing Oracle Data Miner  
10. Summary