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Predictive Analytics using Oracle Data Mining (D91316)

Detaillierter Kursinhalt

Introduction
  • Course Objectives
  • Suggested Course Prerequisites
  • Suggested Course Schedule
  • Class Sample Schemas
  • Practice and Solutions Structure
  • Review location of additional resources
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
Understanding the Data Mining Process
  • Common Tasks in the Data Mining Process
  • Introducing the SQL Developer interface
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
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
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
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
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
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
Mining Structured and Unstructured Data
  • Dealing with Transactional Data
  • Handling Aggregated (Nested) Data
  • Joining and Filtering data
  • Enabling mining of Text
  • Examining Predictive Results
Using Predictive Queries
  • What are Predictive Queries?
  • Creating Predictive Queries
  • Examining Predictive Results
Deploying Predictive models
  • Requirements for deployment
  • Deployment Options
  • Examining Deployment Options