Introduction to IBM SPSS Modeler and Data Mining (V16) Eğitimi

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Eğitim Tipi ve Süresi

2 Days ILT    
Eğitim Sağlayıcı ve Kategori

IBM  » IBM SPSS Modeler Eğitimler
Business Analysis  » Business Analysis Eğitimler

Introduction to IBM SPSS Modeler and Data Mining (V16) 

Introduction to IBM SPSS Modeler and Data Mining (V16) is a two day course, that provides an overview of data mining and the fundamentals of using IBM SPSS Modeler. The principles and practice of data mining are illustrated using the CRISP-DM methodology. The course structure follows the stages of a typical data mining project, from collecting data, to data exploration, data transformation, and modeling to effective interpretation of the results. The course provides training in the basics of how to read, prepare, and explore data with IBM SPSS Modeler, and introduces the student to modeling.

.Who Needs to Attend

This basic course is for:

Anyone who is new to IBM SPSS Modeler
Anyone considering purchasing IBM SPSS Modeler
Anyone interested in Data Mining
Prerequisites

You should have:

General computer literacy

Follow-On Courses

There are no follow-ons for this course.

Course Outline

Introduction to Data Mining

List two applications of data mining
Explain the stages of the CRISP-DM process model
Describe successful data-mining projects and the reasons why projects fail
Describe the skills needed for data mining
Working with Modeler

Describe the MODELER user-interface
Work with nodes
Run a stream or a part of a stream
Open and save a stream
Use the online Help
A Data-Mining Tour

Explain the basic framework of a data-mining project
Build a model
Deploy a model
Collecting Initial Data

Explain the concepts ''data structure'', ''unit of analysis'', ''field storage'' and ''field measurement level''
Import Microsoft Excel files
Import IBM SPSS Statistics files
Import text files
Import from databases
Export data to various formats
Understanding your Data

Audit the data
Explain how to check for invalid values
Take action for invalid values
Explain how to define blanks
Setting the Unit of Analysis

Set the unit of analysis by removing duplicate records
Set the unit of analysis by aggregating records
Set the unit of analysis by expanding a categorical field into a series of flag fields
Integrating Data

Integrate data by appending records from multiple datasets
Integrate data by merging fields from multiple datasets
Sample records
Deriving and Reclassifying Fields

Use the Control Language for Expression Manipulation (CLEM)
Derive new fields
Reclassify field values
Looking for Relationships

Examine the relationship between two categorical fields
Examine the relationship between a categorical field and a continuous field
Examine the relationship between two continuous fields
Introduction to Modeling

List three modeling objectives
Use a classification model
Use a segmentation model

 

 



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