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Handling Missing Data with Imputations in R

AdvancedSkill Level
4.7+
19 reviews
Updated 10/2022
Diagnose, visualize and treat missing data with a range of imputation techniques with tips to improve your results.
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RData Manipulation4 hours13 videos49 Exercises4,200 XP5,459Statement of Accomplishment

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Course Description

Missing data is everywhere. The process of filling in missing values is known as imputation, and knowing how to correctly fill in missing data is an essential skill if you want to produce accurate predictions and distinguish yourself from the crowd. In this course, you’ll learn how to use visualizations and statistical tests to recognize missing data patterns and how to impute data using a collection of statistical and machine learning models. You’ll also gain decision-making skills, helping you decide which imputation method fits best in a particular situation. Finally, you’ll learn to incorporate uncertainty from imputation into your inference and predictions, making them more robust and reliable.

Prerequisites

Intermediate Regression in RDealing With Missing Data in R
1

The Problem of Missing Data

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2

Donor-Based Imputation

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3

Model-Based Imputation

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4

Uncertainty from Imputation

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Handling Missing Data with Imputations in R
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Don’t just take our word for it

*4.7
from 19 reviews
84%
11%
5%
0%
0%
  • LUIS ANGEL
    about 4 hours

  • Elyana
    2 days

  • ADRIAN
    2 days

    A practical and well-structured course that deepened my understanding of handling missing data using various imputation techniques in R. The hands-on approach and real-world datasets made the concepts easy to apply in data analysis workflows. Highly recommended for anyone working with incomplete datasets.

  • NICOLAS RAFAEL
    4 days

  • Minsu
    13 days

  • Shanlong
    30 days

LUIS ANGEL

"A practical and well-structured course that deepened my understanding of handling missing data using various imputation techniques in R. The hands-on approach and real-world datasets made the concepts easy to apply in data analysis workflows. Highly recommended for anyone working with incomplete datasets."

ADRIAN

Minsu

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