Engineer, Validate, and Govern ML Data

This short course helps you build and validate ML-ready data pipelines with confidence. You’ll start by learning how to design ETL workflows that ingest, clean, and partition large datasets using tools like Airflow and Spark. You’ll see how real teams manage click-stream logs, handle nulls, and prepare partitioned training data at scale. Next, you’ll evaluate data quality, governance, and lineage so your pipelines remain trustworthy and reproducible. You’ll work with practical techniques like schema drift checks, expectations suites, and audit-ready lineage records. Through short videos, applied readings, hands-on practice, and a final graded assessment, you’ll walk away knowing how to engineer reliable pipelines and validate them for production use

• Build reliable, ML-ready data pipelines
• Validate data quality and pipeline sustainability
• Apply data governance and drift monitoring techniques
• Develop practical skills through hands-on practice and assessments

• Basic familiarity with ML and data analysis terminology
• Readiness for applied exercises and practice