IBM SPSS Modeler: Techniques for Missing Data

IBM SPSS Modeler Seminar Series
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  • Lectures 20
  • Length 3.5 hours
  • Skill Level Intermediate Level
  • Languages English
  • Includes Lifetime access
    30 day money back guarantee!
    Available on iOS and Android
    Certificate of Completion
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About This Course

Published 4/2014 English

Course 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.

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?

  • 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

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.

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.


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 Values
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-Join
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

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Instructor Biography

Sandy Midili, 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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