IBM SPSS Modeler Foundations (V18.2) SPVC

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

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