Course Description
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.
WHAT YOU'LL LEARN
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
Agenda
COURSE OUTLINE
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
Audience
WHO SHOULD ATTEND?
Business Analysts
Data Scientists