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.
