Question 1

What is missing data?

Accepted Answer

Nothing is perfect, and computers are no exception. Sometimes the data we collect is missing values for a given variable, which can skew analysis and results if not properly addressed.

Question 2

How does missing data affect results

Accepted Answer

Datasets often describe a collection of subjects or events, and missing data can impact our understanding of that population. Learning how to handle missing data (through the methods you’ll learn in this course) will improve your analyses and ability to test your hypotheses.

Question 3

Why is missing data important?

Accepted Answer

Inaccurate data leads to inaccurate conclusions, which can be damaging for a business. Say we’re running a retail chain and want to figure out which products sold the most. If one of our stores doesn’t provide data on the colors of sold products, we might make the wrong conclusion — which could impact budgeting, acquisitions, logistics, and more.

Question 4

What kind of jobs work with missing data?

Accepted Answer

Many popular data careers work with missing data. Data and Machine Learning Engineers need to learn how to handle missing data to build the pipelines that provide data for Data Scientists and Analysts to analyze and build machine learning models with.

Question 5

What else should I study if I am learning about data analysis?

Accepted Answer

Even after you’ve found and fixed all of your missing data, you’ll still need to know how to format it properly so you can use it in your projects and models, so learning how to clean data is a great next step.

Handling Missing Data

About this course

Syllabus

Introduction to Missing Data

Deletion

Imputation

Off-Platform project

4 out of 5 Codecademy learners achieve their goals