Handling Missing Data with Imputations in R Data Camp

Overview

Context

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 Handling Missing Data with Imputations in R course offered by Data Camp, 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.