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IBM SPSS Modeler: Techniques for Missing Data
Rating: 3.8 out of 5(27 ratings)
277 students

IBM SPSS Modeler: Techniques for Missing Data

IBM SPSS Modeler Seminar Series
Created bySandy Midili
Last updated 4/2014
English

What you'll 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

Course content

2 sections • 20 lectures • 3h 16m total length

  • Introduction to Missing Data4:21
  • Missing Data within the context of CRISP-DM5:27
  • Reasons for Missing Information3:44

    Identify why data may be missing, including privacy concerns, sensitive questions, memory lapses, or data entry errors, and remember that some fields may not apply or be lost.

  • Type and Amount of Missing Data6:33
  • Missing Data Issues6:19
  • Ways to Address Missing Data10:13
  • Missing Data Definitions2:18
  • Useful Nodes to Handle Missing Values14:48
  • A First Look at the Data9:47
  • Removing Fields and Records27:09
  • Creating Null Flags11:56
  • Imputing with the Data Audit Node24:47
  • Using Full and Partial Data8:05
  • Imputing the Median and the Mean10:51
  • Using the Anti-Join11:12

Requirements

  • Knowledge or experience with IBM SPSS Modeler or completion of an introductory level data mining course and on the job data mining experience.

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.

Who this course is for:

  • 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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