After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
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.
Prerequisites
Curriculum
Course outline
1
Connecting to Data
Understand why Great Expectations (GX) is such a powerful tool for monitoring data quality. Get familiar with the basics of GX, including how to start a session using a Data Context, and how to load in a pandas dataframe using a Data Source, Data Asset, and Batch Definition.
2
Establishing Expectations
Create and evaluate basic shape and schema Expectations. Validate your Expectations either individually, as part of an Expectation Suite with a Batch Definition, or using a Validation Definition.
3
GX in Practice
Learn practical skills that will help you dominate the dynamic nature of Expectations in the real world. Deploy Validation Definitions using Checkpoints; update your Expectation Suites; and learn how to add, retrieve, list, and delete key GX components.
4
All About Expectations
Dive head-first into the world of Expectations. Practice creating basic column Expectations, row- and aggregate-level numeric Expectations, string and string parseability Expectations, and more. Learn how to apply Expectations to only some rows of a dataframe.
Introduction to Data Quality with Great Expectations
Course
Complete
