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IBM SPSS Modeler: Techniques for Missing Data

IBM SPSS Modeler Seminar Series
4.1 (7 ratings)
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150 students enrolled
Created by Sandy Midili
Last updated 4/2014
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  • 3.5 hours on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What Will I 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
View Curriculum
  • Knowledge or experience with IBM SPSS Modeler or completion of an introductory level data mining course and on the job data mining experience.

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.

Who 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.
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Curriculum For This Course
Expand All 20 Lectures Collapse All 20 Lectures 03:16:21
Missing Data Seminar
15 Lectures 02:37:30
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

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

Question and Answer Session
5 Lectures 38:51
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
About the Instructor
3.6 Average rating
92 Reviews
585 Students
10 Courses
Business Analytics Training Manager

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:

  • IBM Cognos Business Intelligence
  • IBM Cognos TM1
  • IBM Cognos Planning
  • IBM Cognos Finance

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.

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