What is data cleaning?

Raw data is messy and hard to analyze, so we have to “clean” it by filling in missing values, correcting mistakes and improper formatting, fitting it into the right rows and columns, and other methods you’ll learn in this course.

Why do we need to clean data?

Data needs to be prepped and formatted before you can use it. You’ll come across a lot of missing and inaccurate values as you go through raw data, and they can throw off your analyses if left unresolved.

How do you clean data?

Most of the work around cleaning data involves finding and fixing incomplete or missing values. Once that’s handled and the data’s (mostly) accurate, it needs to be shaped and organized into rows and columns.

Who’s responsible for cleaning data?

There’s a saying that Data Scientists spend 80% of their time cleaning and manipulating data and only 20% of their time analyzing it or building models from it, and Machine Learning Engineers spend much of their time building and maintaining pipelines that automatically collect, clean, and organize data into a usable format. Outside of tech, there are a ton of jobs that involve (or can benefit from) working with data, and cleaning data is one of the first steps toward putting it to good use.

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

Learning a little about statistics can help you sharpen your data skills. There are a couple of popular methods for cleaning data that you’ll explore in your studies, and they’ll take you pretty far — but there are various statistical methods you can use when working with numerical data that all have different effects on your analyses.

Course

How to Clean Data with Python

Learn the basics of regular expressions and how to pull and clean data from the web with Python.

This course includes

20,849 learners enrolled

This course includes

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

3 hours
Projects
1
Prerequisites
1 course

We suggest you complete the following courses before you get started with How to Clean Data with Python:
Learn Python 3

About this course

Data cleaning is a crucial skill for data scientists. In this course, you’ll learn techniques to clean messy datasets and get them ready for analysis. You’ll also explore Regular Expressions (Regex), a powerful tool for finding patterns in data. This course equips you with the foundational skills to prepare your data for further analysis.

Skills you'll gain

Clean and preprocess datasets
Use Regular Expressions (Regex)
Apply data cleaning techniques with Python
Handle common data issues

Syllabus

How to Clean Data with Python
Some say data scientists spend 80% of their time cleaning data and 20% of their time doing analysis. Learn how to do this 80% in Python!

lesson Introduction to Regular Expressions
quiz Introduction to Regular Expressions
lesson How to Clean Data with Python
quiz Data Cleaning in Python
project Cleaning US Census Data
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Projects in this course

practice Project
Cleaning US Census Data
Use your pandas and regex skills to clean up this dataset containing a bunch of census data collected by the US government.

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