 班級人數--熱線:4008699035 手機:15921673576( 微信同號) |
| 增加互動環節，
保障培訓效果，堅持小班授課，每個班級的人數限3到5人，超過限定人數,安排到下一期進行學習。 |
| 授課地點及時間 |
上課地點：【上?！浚和瑵髮W(滬西)/新城金郡商務樓(11號線白銀路站) 【深圳分部】：電影大廈(地鐵一號線大劇院站)/深圳大學成教院 【北京分部】：北京中山學院/福鑫大樓 【南京分部】：金港大廈(和燕路) 【武漢分部】：佳源大廈（高新二路） 【成都分部】：領館區1號（中和大道） 【廣州分部】：廣糧大廈 【西安分部】：協同大廈 【沈陽分部】：沈陽理工大學/六宅臻品 【鄭州分部】：鄭州大學/錦華大廈 【石家莊分部】：河北科技大學/瑞景大廈
開班時間(連續班/晚班/周末班）：2020年3月16日 |
| 課時 |
◆資深工程師授課
☆注重質量
☆邊講邊練
☆若學員成績達到合格及以上水平，將獲得免費推薦工作的機會
★查看實驗設備詳情，請點擊此處★ |
| 質量以及保障 |
☆
1、如有部分內容理解不透或消化不好，可免費在以后培訓班中重聽；
☆ 2、在課程結束之后,授課老師會留給學員手機和E-mail,免費提供半年的課程技術支持，以便保證培訓后的繼續消化；
☆3、合格的學員可享受免費推薦就業機會。
☆4、合格學員免費頒發相關工程師等資格證書，提升您的職業資質。 |
☆課程大綱☆ |
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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 Mode
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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