IBM SPSS Modeler: Techniques for Missing Data
IBM SPSS Modeler Seminar Series
What you will learn
Understand how missing data is identified and defined in IBM SPSS Modeler
Impute missing values
Remove missing data
Run parallel streams with and without missing data
Use the Type, Data Audit, Derive, and Filler nodes to identify and handle missing data
Description
IBM SPSS Modeler is a data mining workbench that allows you to build predictive models quickly and intuitively without programming. Analysts typically use SPSS Modeler to analyze data by mining historical data and then deploying models to generate predictions for recent (or even real-time) data.
Overview: Techniques for Missing Data is a series of self-paced videos (three hours of content). Students will learn how missing data is identified and handled in Modeler. Students also will learn different approaches to dealing with missing data including imputation of missing values, removing missing data, and running parallel streams with and without missing data. Students will also learn how to use the Type, Data Audit, and Filler nodes to identify and handle missing data.