Oracle Database 11g: Data Mining Techniques - Fudgelearn

Oracle Database 11g: Data Mining Techniques

Date
Time
Price
Date
Time
Price
Location
Date
Start time
Price
Location
Version title
Price
Oracle Database 11g: Data Mining Techniques
£ 882
Oracle Database 11g: Data Mining Techniques
£ 882

In this course, students review the basic concepts of data mining and learn how leverage the predictive analytical power of the Oracle Database Data Mining option by using Oracle Data Miner 11g Release 2. The Oracle Data Miner GUI is an extension to Oracle SQL Developer 3.0 that enables data analysts to work directly with data inside the database.

The Data Miner GUI provides intuitive tools that help you to explore the data graphically, build and evaluate multiple data mining models, apply Oracle Data Mining models to new data, and deploy Oracle Data Mining's predictions and insights throughout the enterprise. Oracle Data Miner's SQL APIs automatically mine Oracle data and deploy results in real-time. Because the data, models, and results remain in the Oracle Database, data movement is eliminated, security is maximized and information latency is minimized.

  • Analyst
  • Database Administrator
  • Developer
  • Explain basic data mining concepts and describe the benefits of predictive analysis
  • Understand primary data mining tasks, and describe the key steps of a data mining process
  • Use the Oracle Data Miner to build,evaluate, and apply multiple data mining models
  • Use Oracle Data Mining's predictions and insights to address many kinds of business problems, including: Predict individual behavior, Predict values, Find co-occurring events
  • Learn how to deploy data mining results for real-time access by end-users
  • Introduction
    • Course Objectives
    • Suggested Course Pre-requisites
    • Suggested Course Schedule
    • Class Sample Schemas
    • Practice and Solutions Structure
    • Review location of additional resources (including ODM and SQL Developer documentation and online resources)
  • Overviewing Data Mining Concepts
    • 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 Oracle Data Miner 11g Release 2
    • Data mining with Oracle Database
    • Introducing the SQL Developer interface
    • 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
    • Creating Classification Models
    • Building the Models
    • Examining Class Build Tabs
    • Comparing the Models
    • Selecting and Examining a Model
  • 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
  • Performing Market Basket Analysis
    • What is Market Basket Analysis?
    • Reviewing Association Rules
    • Creating a New Workflow
    • Adding a Data Source to th Workflow
    • Creating an Association Rules Model
    • Defining Association Rules
    • Building the Model
    • Examining Test Results
  • 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 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
  • Deploying Data Mining Results
    • Requirements for deployment
    • Deployment Tasks
    • Examining Deployment Options
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Oracle Database 11g: Data Mining Techniques

Course Overview

In this course, students review the basic concepts of data mining and learn how leverage the predictive analytical power of the Oracle Database Data Mining option by using Oracle Data Miner 11g Release 2. The Oracle Data Miner GUI is an extension to Oracle SQL Developer 3.0 that enables data analysts to work directly with data inside the database.

The Data Miner GUI provides intuitive tools that help you to explore the data graphically, build and evaluate multiple data mining models, apply Oracle Data Mining models to new data, and deploy Oracle Data Mining's predictions and insights throughout the enterprise. Oracle Data Miner's SQL APIs automatically mine Oracle data and deploy results in real-time. Because the data, models, and results remain in the Oracle Database, data movement is eliminated, security is maximized and information latency is minimized.

Training on Demand
Date
Start time
Price
Location
05-Mar-2019
06:21 PM GMT
£ 882
06-Mar-2019
12:11 PM GMT
£ 882
Audience
  • Analyst
  • Database Administrator
  • Developer
Objectives
  • Explain basic data mining concepts and describe the benefits of predictive analysis
  • Understand primary data mining tasks, and describe the key steps of a data mining process
  • Use the Oracle Data Miner to build,evaluate, and apply multiple data mining models
  • Use Oracle Data Mining's predictions and insights to address many kinds of business problems, including: Predict individual behavior, Predict values, Find co-occurring events
  • Learn how to deploy data mining results for real-time access by end-users
Topics
  • Introduction
    • Course Objectives
    • Suggested Course Pre-requisites
    • Suggested Course Schedule
    • Class Sample Schemas
    • Practice and Solutions Structure
    • Review location of additional resources (including ODM and SQL Developer documentation and online resources)
  • Overviewing Data Mining Concepts
    • 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 Oracle Data Miner 11g Release 2
    • Data mining with Oracle Database
    • Introducing the SQL Developer interface
    • 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
    • Creating Classification Models
    • Building the Models
    • Examining Class Build Tabs
    • Comparing the Models
    • Selecting and Examining a Model
  • 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
  • Performing Market Basket Analysis
    • What is Market Basket Analysis?
    • Reviewing Association Rules
    • Creating a New Workflow
    • Adding a Data Source to th Workflow
    • Creating an Association Rules Model
    • Defining Association Rules
    • Building the Model
    • Examining Test Results
  • 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 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
  • Deploying Data Mining Results
    • Requirements for deployment
    • Deployment Tasks
    • Examining Deployment Options