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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.
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|Section 1: Missing Data Seminar|
Introduction to Missing Data
Missing Data within the context of CRISP-DM
Reasons for Missing Information
Type and Amount of Missing Data
Missing Data Issues
Ways to Address Missing Data
Missing Data Definitions
Useful Nodes to Handle Missing ValuesPreview
A First Look at the Data
Removing Fields and Records
Creating Null Flags
Imputing with the Data Audit Node
Using Full and Partial Data
Imputing the Median and the Mean
Using the Anti-JoinPreview
|Section 2: Question and Answer Session|
Question and Answer Introduction
Using a Model to Replace Missing Values
When Missing Data Exceeds a Reasonable Amount
Capturing Comments in a Stream
Using a Holdout Sample when Imputing
I have been the Business Analytics Training Manager at QueBIT Consulting since 2006. I am a certified technical trainer and am certified in IBM Cognos products. I can assist you with coordinating a specific training program that will meet your organization's specific educational goals. In addition, I also provide training in:
QueBIT's training program is unique because we can tailor our material to your application and make sure we cover the concepts important to you and your personnel. We don't only teach you how to use the solution, but also guide you with proven best practices and tips and instruct you on how to problem solve issues enabling you to become self-sufficient with the tool.
Prior to joining QueBIT, I was a Financial Performance Instructor for Cognos Corporation. During my seven year tenure, I also became a third-level support specialist for Cognos Finance and Cognos Planning.