Wrap-Up

1. Wrap-Up

Congratulations on completing this introductory course on data quality in Great Expectations! You've put in a lot of work, and it shows. Let's recap what you learned in this course.

2. Chapter 1: Connecting to Data

In Chapter 1, you learned how to create a Data Context as the primary entry point for a GX deployment.

3. Chapter 1: Connecting to Data

You also learned how to create a pandas Data Source and Data Asset to instruct GX how to connect to your data.

4. Chapter 1: Connecting to Data

Finally, you learned how to create a Batch Definition and Batch to actually read in a pandas DataFrame.

5. Chapter 2: Establishing Expectations

In Chapter 2, you learned how to create your first Expectation, and how to validate it using the `batch.validate()` method.

6. Chapter 2: Establishing Expectations

You also learned about a few shape- and schema-related Expectations.

7. Chapter 2: Establishing Expectations

You then learned how to organize your Expectations into Expectation Suites.

8. Chapter 2: Establishing Expectations

Finally, you learned how to validate your Expectation Suites using Validation Definitions.

9. Chapter 3: GX in Practice

In Chapter 3, you learned how to create Checkpoints for grouping together your Validation Definitions and performing Actions in a production deployment of GX.

10. Chapter 3: GX in Practice

You also learned how to copy, delete, and save Expectations.

11. Chapter 3: GX in Practice

Finally, you learned how to add, retrieve, list, and delete your Data Source, Expectation Suite, Validation Definition, and Checkpoint components.

12. Chapter 4: All About Expectations

In Chapter 4, you learned about basic column Expectations at the row and aggregate levels.

13. Chapter 4: All About Expectations

You also learned how to create row- and aggregate-level Expectations specific to numeric type columns, as well as general and parseability Expectations for string type columns.

14. Chapter 4: All About Expectations

Finally, you learned how to create Conditional Expectations to apply Expectations only to certain rows of a Batch DataFrame.

15. Final Words

As you can see, you've learned a lot in this course. Awesome job! Here are some resources where you can find more information, documentation, and learning materials for Great Expectations. While we covered a variety of different Expectations, we've really only touched on a small fraction of all of the Expectations GX has to offer. For a full list of available Expectations, you can visit GX's Expectation Gallery at the link on the slide. One of the aspects of Great Expectations that we didn't touch on are custom Expectations, which are one part of what makes GX so powerful. I encourage you to explore more and play around with creating your own custom Expectations.

16. Congratulations!

Congratulations again on your completion of this course. Great Expectations has been incredibly helpful for monitoring data quality in my career as a data scientist, and I hope you've also gained some valuable skills that will help you with ensuring data quality in whatever ways you use data. Now, let's go make the world a better place, one dataset at a time!

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