Oracle Database 19c: Data Mining Techniques

COURSE OUTLINE

MODULE 01 Introduction

• Introduction

MODULE 02 Data Mining Concepts and Terminology

• Data Mining Concepts and Terminology

MODULE 03 The Data Mining Process

• The Data Mining Process

MODULE 04 Introducing Oracle Data Miner

• Introducing Oracle Data Miner
• Practice 3-1: Creating a User Account for Data Miner in Your Database
• Practice 3-2: Creating a Data Miner User Connection and Installing the Data Miner Repository
• Practice 3-3: Creating a Data Miner Project and Workflow

MODULE 05 Using Classification Models

• Using Classification Models
• Practice 4-1: Selecting and Examining Source Data
• Practice 4-2: Creating Classification Models
• Practice 4-3: Scoring the Data

MODULE 06 Using Regression Models

• Using Regression Models
• Practice 5-1: Creating a New Workflow and Examining the Source Data
• Practice 5-2: Creating Regression Models
• Practice 5-3: Scoring the Data

MODULE 07 Using Clustering Models

• Using Clustering Models
• Practice 6-1: Creating Clustering Models
• Practice 6-2: Viewing Model Results and Applying the Model

MODULE 08 Performing Market Basket Analysis

• Performing Market Basket Analysis
• Practice 7-1: Creating a New Workflow and Examining the Source Data
• Practice 7-2: Creating an Association Rules Model
• Practice 7-3: Modifying the Source Data and Rebuilding the Model
• Practice 7-4: Applying Model Rule Filters and Creating Model Details

MODULE 09 Performing Anomaly Detection

• Performing Anomaly Detection
• Practice 8-1: Creating an Anomaly Detection Model
• Practice 8-2: Viewing Model Results and Applying the Model

MODULE 10 Deploying Data Mining Results

• Deploying Data Mining Results
• Practice 9-1: Creating a Table for Data Mining Results
• Practice 9-2: Deploying the SQL Code for Data Mining Results
• Practice 9-3: Modifying Classification Models and Redeploying the Code