IBM SPSS Modeler Foundations (V18.2) [TDS_0A069G]

OVERVIEW

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.

Virtual Learning

This interactive training can be taken from any location, your office or home and is delivered by a trainer. This training does not have any delegates in the class with the instructor, since all delegates are virtually connected. Virtual delegates do not travel to this course, Global Knowledge will send you all the information needed before the start of the course and you can test the logins.

OBJECTIVES

Please refer to course overview.

AUDIENCE

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

CONTENT

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 dataCollect initial data• Describe field storage• Describe field measurement level• Import from various data formats• Export to various data formatsUnderstand the data• Audit the data• Check for invalid values• Take action for invalid values• Define blanksSet the unit of analysis• Remove duplicates• Aggregate data• Transform nominal fields into flags• Restructure dataIntegrate data• Append datasets• Merge datasets• Sample recordsTransform fields• Use the Control Language for Expression Manipulation• Derive fields• Reclassify fields• Bin fieldsFurther field transformations• Use functions• Replace field values• Transform distributionsExamine relationships• Examine the relationship between two categorical fields• Examine the relationship between a categorical  and continuous field• Examine the relationship between two continuous fieldsIntroduction to modeling• Describe modeling objectives• Create supervised models• Create segmentation modelsImprove 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