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Oracle Database 12c: データ・マイニング手法

コース基本情報

コースタイトル Oracle Database 12c: データ・マイニング手法
コースコード RAC0133R  
コース種別 集合研修 形式 講義+実機演習
期間 2日間 時間 9:30~17:30 価格(税込) 157,080円(税込)
主催 日本オラクル
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対象者情報

対象者
・Database Administrators
・Data Scientist
・Data Analyst
前提条件
□No prerequisites equired

学習内容の詳細

コース概要
This Predictive Analytics using Oracle Data Mining Ed 1 training will review the basic concepts of data mining. Expert Oracle University instructors will teach you how to leverage the predictive analytical power of Oracle Data Mining, a component of the Oracle Advanced Analytics option.

Learn To:
• 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, apply, and deploy multiple data mining models.
• Use Oracle Data Mining's predictions and insights to address many kinds of business problems.
• Deploy data mining models for end-user access, in batch or real-time, and within applications.

Benefits to You
When you've completed this course, you'll be able to use the Oracle Data Miner 4.1, the Oracle Data Mining workflow GUI, which enables data analysts to work directly with data inside the database. The Data Miner GUI provides intuitive tools that help you 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 - Get Results in Real-Time
Oracle Data Miner's SQL APIs automatically mine Oracle data and deploy results in realtime.
Because the data, models, and results remain in the Oracle Database, data movement is eliminated, security is maximized and information latency is minimized.
学習目標
● 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, apply, and deploy multiple data mining models
● Use Oracle Data Mining's predictions and insights to address many kinds of business problems
● Deploy data mining models for batch or real-time access by end-users
学習内容
1. Introduction
  - Review location of additional resources
  - Course Objectives
  - Practice and Solutions Structure
  - Suggested Course Prerequisites
  - Class Sample Schemas
  - Suggested Course Schedule

2. Predictive Analytics and Data Mining Concepts
  - Introducting the Oracle Advanced Analytics (OAA) Option?
  - Why use Data Mining?
  - Supervised Versus Unsupervised Learning
  - Supported Data Mining Algorithms and Uses
  - What is Data Mining?
  - What is the Predictive Analytics?
  - Examples of Data Mining Applications

3. Understanding the Data Mining Process
  - Common Tasks in the Data Mining Process
  - Introducing the SQL Developer interface

4. Introducing Oracle Data Miner 4.1
  - Previewing Data Miner Workflows
  - Examining Data Miner Nodes
  - Setting up Oracle Data Miner
  - Data mining with Oracle Database
  - Identifying Data Miner interface components
  - Accessing the Data Miner GUI

5. Using Classification Models
  - Building the Models
  - Using the Data Source Wizard
  - Examining Class Build Tabs
  - Adding a Data Source to the Workflow
  - Creating Classification Models
  - Reviewing Classification Models
  - Using the Column Filter Node
  - Using Explore and Graph Nodes

6. Using Regression Models
  - Using the Data Source Wizard
  - Selecting a Model
  - Building the Models
  - Comparing the Models
  - Creating Regression Models
  - Reviewing Regression Models
  - Performing Data Transformations
  - Adding a Data Source to the Workflow

7. Using Clustering Models
  - Adding Data Sources to the Workflow
  - Comparing Model Results
  - Exploring Data for Patterns
  - Describing Algorithms used for Clustering Models
  - Defining and Building Clustering Models
  - Defining Output Format
  - Selecting and Applying a Model
  - Examining Cluster Results

8. Performing Market Basket Analysis
  - Adding a Data Source to the Workflow
  - Creating a New Workflow
  - What is Market Basket Analysis?
  - Reviewing Association Rules
  - Building the Model
  - Defining Association Rules
  - Creating an Association Rules Model
  - Examining Test Results

9. Performing Anomaly Detection
  - Applying the Model
  - Adding Data Sources to the Workflow
  - Evaluating Results
  - Building the Model
  - Examining Test Results
  - Reviewing the Model and Algorithm used for Anomaly Detection
  - Creating the Model

10. Mining Structured and Unstructured Data
  - Enabling mining of Text
  - Joining and Filtering data
  - Examining Predictive Results
  - Handling Aggregated (Nested) Data
  - Dealing with Transactional Data

11. Using Predictive Queries
  - Examining Predictive Results
  - Creating Predictive Queries
  - What are Predictive Queries?

12. Deploying Predictive models
  - Examining Deployment Options
  - Requirements for deployment
  - Deployment Options

ご注意・ご連絡事項

・本コースをお申し込みの場合、上記の「日本オラクル開催コースの規約」を必ずご覧ください。

・オラクル認定コースにお申し込みいただいた方・申込責任者の方の個人情報は、第三者である日本オラクル株式会社と共有させていただきます。あらかじめご了承ください。
日本オラクル株式会社開催コースの受講お申し込みは8日前まで、キャンセル・日程変更の受講料の扱いは以下のとおりです。

コース開始日の7日前までにコース申し込みを取り消しまたは日程変更した場合 受講料の請求なし
コース開始前6日以内にコース申し込みを取り消しまたは日程変更した場合 受講料の50%を請求
コースに欠席またはコース当日に取り消した場合 受講料の全額を請求
・この研修コースのテキストは、電子ファイル教材「eKit」で提供します。