Dealing with missing data

Handling missing data can take many forms. Removing them in a proper way or substituting their values can be a tricky task to accomplish. Using the presented resources you can gain confidence in handling missing data and make your dataset more consistent and coherent.  

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Resource: Dealing with Missing Values in R 

Type:
Who it’s for: Learners able to create and perform basic operations on datasets.  
Why we love it: a video made by the technology leader IBM that is a part of a larger course. It exposes the learner to a good new resource for learning and allows for diversification of resources.   

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Resource: Data Cleaning with R and the Tidyverse: Detecting Missing Values 

Type:
Who it’s for: Learner’s able to create and perform basic operations on datasets.  
Why we love it: a learner friendly article on how to explore the world of missing data. 

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Resource: Data Wrangling Exercise 

Type:
Who it’s for: Learner’s able to create and perform basic operations on datasets.  
Why we love it: An exercise that helps learners to get hands on experience with missing data.