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Data Quality Testing Unleashed : Theory to Implementation
645 students
Data Quality Testing Unleashed : Theory to Implementation
Learn Essential Data Quality Principles, Implement Testing with Python and Great Expectations Framework
Created byNorth (CJ) Star
Last updated 11/2025
English
What you'll learn
- Gain a clear understanding of the essential principles of Data Quality and Data Quality Testing, equipping you with the knowledge to delivering Quality Data.
- Build robust Data Quality Testing workflows using the Great Expectations, mastering the design and automation of tests to ensure outstanding data quality.
- Explore the Great Expectations testing framework, gaining insights into its foundational components and how they work together to ensure robust data validation.
- Develop thorough data documentation and automate actions that respond to published data quality test results, ensuring proactive management of data quality.
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Course content
9 sections • 65 lectures • 3h 24m total length
Requirements
- Experience with Python: A basic understanding of Python programming is required, as the course involves hands-on implementations using Python with Great Expectations.
- Basic Understanding of Data Concepts: Familiarity with fundamental data concepts like database, data pipeline, etc
- Interest in Data Quality Testing : A willingness to learn about data validation and testing processes will be beneficial for maximising the course outcomes.
Description
Data Quality Testing Unleashed: From Theory to Implementation is your comprehensive roadmap to mastering Data Quality Testing using Python and the powerful Great Expectations framework. It is designed for those who want to elevate their data projects by ensuring high-quality and reliable data. This course takes you from foundational principles to hands-on implementation.
In this course, we'll explore:
Fundamentals of Data Quality & Testing: Discover the core principles that underpin data quality and testing, with a focus on critical dimensions like accuracy, completeness, and consistency. You’ll understand how these elements contribute to trustworthy, dependable data.
Introduction to the Great Expectations Framework: Gain proficiency with Great Expectations, the leading open-source tool for data validation, documentation, and profiling. This framework is crafted to set and enforce data standards, ensuring that data meets the highest quality benchmarks.
The Building Blocks of Great Expectations: Uncover the core components of Great Expectations, learning how to structure workflows that bring them to life. You’ll dive into the extensive expectations library, equipping yourself with versatile tools to meet diverse data validation needs.
Hands-On Data Quality Testing: With a focus on practical application, this course will guide you through creating multiple testing workflows. You’ll learn how to publish results, automate actions based on test outcomes, and build experience in efficiently managing data quality testing in real-world scenarios.
By the end of this course, you’ll have a thorough understanding of data quality testing principles and hands-on skills in applying the Great Expectations framework. You’ll be ready to deliver data that meets rigorous quality standards and confidently contribute to any data project with best-in-class testing practices.
Who this course is for:
- Data Engineers / Testers : Implement effective data quality practices.
- Data Project/Product Managers: Essential knowledge to oversee data quality initiatives effectively.
- Anyone who is interested to learn more about data quality and testing