IBM SPSS Modeler Foundations (V18.2)

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.

Nach Abschluss dieses Seminars haben die Teilnehmer Wissen zu folgenden Themen:
 

• Introduction to IBM SPSS Modeler 
• Collect initial data 
• Understand the data 
• Set the unit of analysis 
• Integrate data 
• Transform fields
• Further field transformations 
• Examine relationships 
• Introduction to modeling
• Improve efficiency  

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

Knowledge of your business requirements

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