IBM SPSS Modeler: Techniques for Missing Data

IBM SPSS Modeler Seminar Series

3.85 (27 reviews)
Udemy
platform
English
language
Data & Analytics
category
instructor
274
students
3.5 hours
content
Apr 2014
last update
$19.99
regular price

What you will 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

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.

Content

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

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

Screenshots

IBM SPSS Modeler: Techniques for Missing Data - Screenshot_01IBM SPSS Modeler: Techniques for Missing Data - Screenshot_02IBM SPSS Modeler: Techniques for Missing Data - Screenshot_03IBM SPSS Modeler: Techniques for Missing Data - Screenshot_04

Reviews

Sebastian
December 26, 2017
nice course, woth the money. many interesting cases with good explanations. however, some study material would be a nice add on.
Cristian
March 24, 2017
I cannot download the dataset file: cup98_mod.txt used in the presentation so I can do my own stream. Anyway, good tutorial overall.
Aura
January 7, 2017
Pros: One of the few advanced courses on Udemy in terms of Statistitcs. Cons: there are two guys that recorded themselves while presenting their workshop on missing/imputing values to other companies. It would have been nice to see a bespoke class only for Udemy students. Also there are moments when the leading guy is asking the second guy for backup and questions, which is not making them very credible.

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Related Topics

196902
udemy ID
4/10/2014
course created date
1/29/2021
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