Data integrity is critical to successful analysis. In this course, you’ll explore methods and steps that analysts take to check their data for integrity. This includes knowing what to do when you don’t have enough data. You’ll also learn about sample size and understand how to avoid sampling bias. All of these methods will help you ensure your analysis is successful.



The Importance of Integrity
This course is part of Google Data-Driven Decision Making Specialization

Instructor: Google Career Certificates
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Access provided by Workforce Innovation Network (WIN) LLC
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What you'll learn
Define different types of data integrity and identify risks to data integrity.
Describe the benefits of documenting data cleaning process.
Describe strategies that can be used to address insufficient data.
Describe the benefits of documenting the data cleaning process
Skills you'll gain
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6 assignments
September 2025
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There are 4 modules in this course
Data integrity is critical to successful analysis. In this module, you’ll explore methods and steps that analysts take to check their data for integrity.
What's included
2 videos3 readings1 assignment
In this module, you will learn about the importance of sample size and how to deal with insufficient data. You’ll also learn about pre-cleaning activities that must be performed before running data analysis.
What's included
2 videos2 readings2 assignments
This module explores strategies to test the data and determine the best representative sample size.
What's included
2 videos2 readings1 assignment
This module explores evaluating data reliability and introduces the concept of margin of error.
What's included
1 video1 reading2 assignments
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