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.


您将学到什么
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
您将获得的技能
要了解的详细信息

添加到您的领英档案
September 2025
6 项作业
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该课程共有4个模块
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.
涵盖的内容
2个视频3篇阅读材料1个作业
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.
涵盖的内容
2个视频2篇阅读材料2个作业
This module explores strategies to test the data and determine the best representative sample size.
涵盖的内容
2个视频2篇阅读材料1个作业
This module explores evaluating data reliability and introduces the concept of margin of error.
涵盖的内容
1个视频1篇阅读材料2个作业
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常见问题
Data is a group of facts that can take many different forms, such as numbers, pictures, words, videos, observations, and more. We use and create data everyday, like when we stream a show or song or post on social media.
Data analytics is the collection, transformation, and organization of these facts to draw conclusions, make predictions, and drive informed decision-making.
No prior experience with spreadsheets or data analytics is required. All you need is high-school level math and a curiosity about how things work.
You don't need to be a math all-star to succeed in this certificate. You need to be curious and open to learning with numbers (the language of data analysts). Being a strong data analyst is more than just math, it's about asking the right questions, finding the best sources to answer your questions effectively, and illustrating your findings clearly in visualizations.
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