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.
Clean Your Data
Clean Your Data
This course is part of Google Data Analysis with Python Specialization
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Gain insight into a topic and learn the fundamentals.
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
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Assessments
5 assignments
Taught in English
Recently updated!
September 2025
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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.
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There are 5 modules in this course
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
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
Review everything you’ve learned and take the final assessment.
What's included
1 reading1 assignment
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