FAQ: Data Types and Quality - Working with Missing Data

This community-built FAQ covers the “Working with Missing Data” exercise from the lesson “Data Types and Quality”.

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I don’t quite understand the difference between Missing completely at random and Missing at random.

So the former just means data wasn’t entered properly. What about the latter? How is it different from the former?


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I found more information in the cheatsheet that answered my question: https://www.codecademy.com/learn/paths/data-science-nlp/tracks/dsf-data-literacy/modules/introduction-to-data-38e13b33-2ba6-4515-bfbf-4a785c9194a9/cheatsheet


The difference is that Missing completely at random is a random error not linked to a variable (i.e bad entry due to fatigue and sloppiness) where Missing at random can have something with a variable causing the error (tree bigger than the tape measure).

Make sure you do the “Handling Missing Data” course that is linked to get deeper into it. The cheat sheet doesn’t have enough detail but the course is good.
Handling Missing Data | Codecademy


Hello there! I didn’t really get what exactly we can do about missing data, what are the steps? Imagine I have such a situation at work, what exactly am I supposed to do? Thank you so much for the answer!

That’s why I recommended the Handling Missing Data course. It teaches you how to fix the different types of problems arising in categorical or numeric data types. I had no problems with it until the last page when it gets heavy into pandas. I also had to learn Jupyter Notebooks and am working on Git and Github to get ready for the first project. There’s a lot to learn.