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Introduction to Data Quality with Great Expectations
4.8(395)3k studentsINTERMEDIATEEnglish
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Course Overview
Ensuring data integrity is critical across data science and engineering pipelines. This intermediate course introduces the Great Expectations Python library, teaching you how to define, manage, and deploy automated assertions to monitor data quality in production environments.
Who Should Enroll?
Designed for intermediate Python users, data engineers, and data scientists seeking to integrate automated data validation and quality monitoring into their data workflows.
Key Takeaways
- Expectation Assertion: Define and apply core Expectations to verify numeric, string, and dataset properties.
- Lifecycle & Pipelines: Retrieve, edit, delete Expectations, and construct automated validation pipelines for production.
- Conditional Rules: Write advanced conditional Expectations linking dependencies across multiple columns.
About this course
Great Expectations is a powerful tool for monitoring data quality in data science and data engineering workflows. The platform can be easily integrated into Python, making it a useful library for Python users to master.
At the core of Great Expectations are Expectations, or assertions that you'd like to verify about your data. You'll begin this course by learning how to connect to real-world datasets and apply Expectations to them. You'll then learn how to retrieve, edit, delete Expectations, and build pipelines for applying Expectations to new datasets in a production deployment.
Finally, you'll learn about specific types of Expectations, such as for numeric and string columns, and how to write Expectations of one column conditional on the values of other columns.
By the end of this course, you'll have a strong foundation in the Great Expectations Python library. You'll be able to use the platform's core functionalities to monitor the quality of your data, and you'll be able to use your data with confidence that it meets your data quality standards.
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