What is missing data?

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.

How does missing data affect results

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.

Why is missing data important?

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.

What kind of jobs work with missing data?

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.

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

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.

Course

Handling Missing Data

Learn how and when to tackle missing data with deletion, single imputation, linear interpolation, and multiple imputation techniques.

This course includes

4,034 learners enrolled

This course includes

Skill level
Beginner
Time to complete
Average based on combined completion rates — individual pacing in lessons, projects, and quizzes may vary

1 hour
Prerequisites
3 courses

We suggest you complete the following courses before you get started with Handling Missing Data:
Data Wrangling and Tidying
Data Wrangling and Tidying
Learn Data Analysis with Pandas

About this course

Missing data is a common challenge in real-world datasets that can affect analysis accuracy. This course teaches how to identify and handle various types of missing data using Python. You’ll learn methods like deletion, linear interpolation, and multiple imputation, ensuring your data is clean and analysis-ready.

Skills you'll gain

Identify and categorize missing data (MCAR, MAR, MNAR)
Apply techniques like deletion, LOCF, and NOCB
Use imputation methods like linear interpolation and multiple imputation

Syllabus

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