IBM SPSS Modeler Foundations (V18.2) SPVC – 0E069G SPVC

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Details


Course Code: 0E069G

Brand: DS&BA – SPSS

Category: Analytics

Skill Level: Basic

Duration: 16.0H

Modality: SPVC

 

 

Audience


  • Data scientists
  • Business analysts
  • Clients who are new to IBM SPSS Modeler or want to find out more about using it

 

Prerequisites


  • Knowledge of your business requirements

 

Short Summary


This course teaches how to use IBM SPSS Modeler 18.2.

 

Overview


Contains PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.

This course provides the foundations of using IBM SPSS Modeler and introduces the participant to data science. The principles and practice of data science are illustrated using the CRISP-DM methodology. The course provides training in the basics of how to import, explore, and prepare data with IBM SPSS Modeler v18.2, and introduces the student to modeling.

If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course. http://www.ibm.com/training/terms

 

Topic


Introduction to IBM SPSS Modeler
• Introduction to data science
• Describe the CRISP-DM methodology
• Introduction to IBM SPSS Modeler
• Build models and apply them to new data

Collect initial data
• Describe field storage
• Describe field measurement level
• Import from various data formats
• Export to various data formats

Understand the data
• Audit the data
• Check for invalid values
• Take action for invalid values
• Define blanks

Set the unit of analysis
• Remove duplicates
• Aggregate data
• Transform nominal fields into flags
• Restructure data

Integrate data
• Append datasets
• Merge datasets
• Sample records

Transform fields
• Use the Control Language for Expression Manipulation
• Derive fields
• Reclassify fields
• Bin fields

Further field transformations
• Use functions
• Replace field values
• Transform distributions

Examine relationships
• Examine the relationship between two categorical fields
• Examine the relationship between a categorical  and continuous field
• Examine the relationship between two continuous fields

Introduction to modeling
• Describe modeling objectives
• Create supervised models
• Create segmentation models

Improve efficiency
• Use database scalability by SQL pushback
• Process outliers and missing values with the Data Audit node
• Use the Set Globals node
• Use parameters
• Use looping and conditional execution

 

Objectives


Introduction to IBM SPSS Modeler 
• Introduction to data science 
• Describe the CRISP-DM methodology 
• Introduction to IBM SPSS Modeler 
• Build models and apply them to new data 
 

Collect initial data 
• Describe field storage 
• Describe field measurement level 
• Import from various data formats 
• Export to various data formats 
 

Understand the data 
• Audit the data 
• Check for invalid values 
• Take action for invalid values 
• Define blanks 
 

Set the unit of analysis 
• Remove duplicates 
• Aggregate data 
• Transform nominal fields into flags 
• Restructure data 
 

Integrate data 
• Append datasets 
• Merge datasets 
• Sample records

 

Transform fields 
• Use the Control Language for Expression Manipulation 
• Derive fields 
• Reclassify fields 
• Bin fields 
 

Further field transformations 
• Use functions 
• Replace field values 
• Transform distributions 
 

Examine relationships 
• Examine the relationship between two categorical fields 
• Examine the relationship between a categorical and continuous field 
• Examine the relationship between two continuous fields 
 

Introduction to modeling 
• Describe modeling objectives 
• Create supervised models 
• Create segmentation models 
 

Improve efficiency 
• Use database scalability by SQL pushback 
• Process outliers and missing values with the Data Audit node 
• Use the Set Globals node 
• Use parameters 
• Use looping and conditional execution

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