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Introduction to Data Quality with Great Expectations

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4 hr
3,500 XP
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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.

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  1. 1

    Connecting to Data

    Free

    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.

    Play Chapter Now
    Create a Data Context
    50 xp
    Create your Data Context
    100 xp
    Sneak Peek into GX
    100 xp
    Keyword Definitions
    100 xp
    Connect to Data
    50 xp
    Concept Review
    100 xp
    Create a Data Source and Data Asset
    100 xp
    Read Data in Batches
    50 xp
    Create a Batch Definition and Batch
    100 xp
    Inspect your Batch
    100 xp
  2. 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.

    Play Chapter Now
    Create Expectations
    50 xp
    Establish a Column Count Expectation
    100 xp
    Evaluate Validation Results
    100 xp
    Schema Expectations
    50 xp
    Establish a Column Name Expectation
    100 xp
    Establish a Column Range Expectation
    100 xp
    Create a Suite of Expectations
    50 xp
    Create an Expectation Suite
    100 xp
    Validate your Expectation Suite
    100 xp
    Validate Expectation Suites
    50 xp
    Create and Run a Validation Definition
    100 xp
    Validation Definition vs. Batch Definition
    100 xp
  3. 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.

    Play Chapter Now
    Deploy Validation Definitions
    50 xp
    Concept Check
    100 xp
    Create a Checkpoint
    100 xp
    Update Expectation Suites
    50 xp
    Copy an Expectation
    100 xp
    Delete an Expectation
    100 xp
    Update an Expectation
    100 xp
    Manage Components
    50 xp
    Add and list components
    100 xp
    Manage a Checkpoint
    100 xp
  4. 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.

    Play Chapter Now
    Basic Column Expectations
    50 xp
    Establish row-level Expectations
    100 xp
    Establish aggregate-level Expectations
    100 xp
    Type-Specific Expectations
    50 xp
    Establish numeric Expectations
    100 xp
    Establish string Expectations
    100 xp
    Conditional Expectations
    50 xp
    Write up a Conditional Expectation
    100 xp
    Invert a Conditional Expectation
    100 xp
    Wrap-Up
    50 xp
For Business

Training 2 or more people?

Get your team access to the full DataCamp platform, including all the features.

datasets

weather_new.csvmovie_dataset.csvlife_expectancy.csvrenewable_new.csvshein_footwear_clean.csv

collaborators

Collaborator's avatar
George Boorman
Collaborator's avatar
Arne Warnke
Collaborator's avatar
Katerina Zahradova

prerequisites

Data Manipulation with pandas
Davina Moossazadeh HeadshotDavina Moossazadeh

Data Scientist & CTO

I'm a data scientist by trade, having worked at companies like Allstate and AmFam. Now, I have my own startup, where I work as the CTO. I'm passionate about leveraging data for health equity, combating climate change, and general social good.
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