About this course
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.
What are the requirements?
- Knowledge or experience with IBM SPSS Modeler or completion of an introductory level data mining course and on the job data mining experience.
What am I going to get from this course?
- Over 20 lectures and 3.5 hours of content!
- 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
What is the target audience?
- Anyone that has experience with IBM SPSS Modeler or has completed an introductory level data mining course and would like to learn about different ways to handle missing data.
