Formation 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.

Objectifs pédagogiques :

À l'issue de cette formation IBM SPSS Modeler Foundations, vous aurez acquis les connaissances et compétences nécessaires pour :

• 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

Public :

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

Prérequis :

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