Clean Your Data

This course is part of Google Data Analysis with Python Specialization

Instructor: Google Career Certificates

Access provided by INEFOP - Instituto Nacional de Empleo y Formación Profesional de Uruguay

5 modules

Gain insight into a topic and learn the fundamentals.

20 reviews

Intermediate level

Some related experience required

6 hours to complete

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

20 reviews

Intermediate level

Some related experience required

6 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Explore the EDA practices of cleaning, validating and joining data

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

5 assignments

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

Build your subject-matter expertise

This course is part of the Google Data Analysis with Python Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 5 modules in this course

In this course, you’ll explore three exploratory data analysis (EDA) practices: cleaning, joining, and validating. You'll discover the importance of these practices for data analysis, and you’ll use Python to clean, validate, and join data.

By the end of this course, you will be able to: • Apply input validation skills to a dataset with Python • Explain the importance of input validation • Demonstrate how to transform categorical data into numerical data with Python • Explain the importance of categorical versus numerical data in a dataset • Explain the importance of recognizing outliers in a dataset • Demonstrate how to identify outliers in a dataset with Python • Understand when to contact stakeholders or engineers regarding missing values • Explain the importance of ethically considering missing values • Demonstrate how to identify missing data with Python

Missing or duplicate data can appear in datasets for numerous reasons. The impact of missing values can vary depending on how many are present. In this module, you will learn strategies to address missing data entries, determine when deduplication is needed, and use common Python functions for handling duplicates.

What's included

4 videos1 reading1 assignment3 ungraded labs

4 videos Total 27 minutes

Introduction to data cleaning 4 minutes
Methods for handling missing data 8 minutes
Work with missing data in a Python notebook 12 minutes
Remy: A day in the life of a data professional 3 minutes

1 reading Total 8 minutes

Data deduplication with Python 8 minutes

1 assignment Total 8 minutes

Test your knowledge: The challenge of missing or duplicate data 8 minutes

3 ungraded labs Total 100 minutes

Annotated follow-along guide: Work with missing data in a Python notebook 20 minutes
Activity: Address missing data 60 minutes
Exemplar: Address missing data 20 minutes

Outliers are data points that stand out amongst others. A tactful approach to outliers recognizes the human stories and real-world effects they represent. In this module, you will learn the types of outliers, how to handle them, and visualize them.

What's included

2 videos2 readings1 assignment

Data models typically work better with numerical inputs. To facilitate this, categorical data is encoded into numeric digits for analysis. In this module, you will learn why this transformation is needed, what dummy variables are, and how to select the right encoding method.

What's included

2 videos2 readings1 assignment

Input validation focuses on thoroughly checking data for completeness and to eliminate errors. In this module, you will learn why validation minimizes errors, how to detect improper inputs, and why it's essential for joining datasets.

What's included

2 videos1 assignment2 ungraded labs1 plugin

2 videos Total 14 minutes

The value of input validation 6 minutes
Input validation with Python 8 minutes

1 assignment Total 6 minutes

Test your knowledge: Input validation 6 minutes

2 ungraded labs Total 80 minutes

Activity: Validate and clean your data 60 minutes
Exemplar: Validate and clean your data 20 minutes

1 plugin Total 10 minutes

Identify: Python functions for cleaning data 10 minutes

Review everything you’ve learned and take the final assessment.

What's included

1 reading1 assignment

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructor

Instructor ratings

(5 ratings)

Google Career Certificates

Google

386 Courses 15,700,156 learners

Offered by

Google

Why people choose Coursera for their career

Felipe M.

Learner since 2018

"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."

Jennifer J.

Learner since 2020

"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."

Larry W.

Learner since 2021

"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."

Chaitanya A.

"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Learner reviews

5 stars
100%
4 stars
0%
3 stars
0%
2 stars
0%
1 star
0%

Showing 3 of 20

Reviewed on Dec 15, 2025

his course helped me understand the basics clearly and improved my practical skills. Well structured and easy to follow.

Reviewed on Nov 20, 2025

From the Python course, I learned the foundational skills of programming, such as writing code, using variables, loops, and functions, and understanding how to solve problems using Python.”

View more reviews