Dive into the practical aspects of data quality in this hands-on course that equips you with hands-on coding skills in a sandbox environment. Data engineer Mark Freeman shows you how to identify, analyze, and resolve data quality issues using robust approaches like root cause analysis and chaos engineering. Discover how to use SQL queries and DBT tests to safeguard data pipelines and ensure the reliability of your datasets. Learn about downstream pipeline investigations and uncover the business logic that affects data quality. Find out how to apply SBAR strategies to convey your insights to stakeholders. Whether you’re a data scientist, analyst, or simply enthusiastic about improving data workflows, this course helps you unlock your potential to solve complex data quality problems while making a significant impact in data-driven environments.
This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that you’ll likely encounter in the workplace. Check out “Using GitHub Codespaces” with this course to learn how to get started.
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