Visualization and Imputation of Missing Data

Learn to create numerous unique visualizations to better understand patterns of missing data in your data sample.
3.63 (38 reviews)
Udemy
platform
English
language
Social Science
category
Geoffrey Hubona, Ph.D.
instructor
Visualization and Imputation of Missing Data
1 312
students
5 hours
content
Sep 2020
last update
$13.99
regular price

What you will learn

Use visualizations created by R software to identify patterns of 'missingness' in data sets and to impute reasonable values to replace the missing data.

Recognize and identify the different patterns of missing data and the relative severity of their likely consequences.

Learn to use the VIM and VIMGUI R packages to create unique, novel and vibrant images which promote the understanding of patterns of both missing and imputed data in a set of data.

Learn the different historical approaches to impute reasonable values for missing data and their relative advantages and disadvantages.

Learn the characteristics of: (1) Hot-Deck; (2) K-Nearest Neighbor; (3) Regression-Based; and (4) Iterative, Model-Based, Stepwise Regression (IRMI) imputation techniques to "fill in" missing data and when and how to implement them with provided software.

Course Gallery

Visualization and Imputation of Missing Data – Screenshot 1
Screenshot 1 – Visualization and Imputation of Missing Data
Visualization and Imputation of Missing Data – Screenshot 2
Screenshot 2 – Visualization and Imputation of Missing Data
Visualization and Imputation of Missing Data – Screenshot 3
Screenshot 3 – Visualization and Imputation of Missing Data
Visualization and Imputation of Missing Data – Screenshot 4
Screenshot 4 – Visualization and Imputation of Missing Data

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Related Topics

631264
udemy ID
05/10/2015
course created date
22/11/2019
course indexed date
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