Module 1: Introduction
- Course Objectives
- Suggested Course Prerequisites
- Suggested Course Schedule
- Class Sample Schemas
- Practice and Solutions Structure
- Review location of additional resources
Module 2: 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
Module 3: Understanding the Data Mining Process
- Common Tasks in the Data Mining Process
- Introducing the SQL Developer interface
Module 4: 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
Module 5: 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
Module 6: 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
Module 7: 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
Module 8: 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
Module 9: 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
Module 10: Mining Structured and Unstructured Data
- Dealing with Transactional Data
- Handling Aggregated (Nested) Data
- Joining and Filtering data
- Enabling mining of Text
- Examining Predictive Results
Module 11: Using Predictive Queries
- What are Predictive Queries?
- Creating Predictive Queries
- Examining Predictive Results
Module 12: Deploying Predictive models
- Requirements for deployment
- Deployment Options
- Examining Deployment Options