Python for Data Engineers
BeginnerDevelopment Environments · 8 lessons · 54 min
Practical Python — data structures, file/API handling and clean code — for engineering, not just scripting.
What this course covers
A focused, no-fluff path through the Python you actually use building data pipelines: core data structures, working with files and APIs, virtual environments and package management, and habits that keep scripts maintainable as they grow.
Priya Nair
Data Engineering Lead
Course outline
Module 1: Core Python for Data Work
- 📄Lists, dicts and comprehensions8 min
- 📄Functions, *args/**kwargs and type hints7 min
- 📄Exception handling that doesn't hide bugs6 min
Module 2: Files, APIs and Environments
- 📄Reading and writing CSV, JSON and Parquet7 min
- 📄Calling APIs with requests7 min
- 📄Virtual environments and requirements files6 min
Module 3: Writing Code That Survives Contact With Real Data
- 📄Logging instead of print statements6 min
- 📄Testing data logic with pytest7 min
📝 Python for Data Engineers Check(pass at 70%)
🔒 Python for Data Engineers — Final Exam(proctored · 30 min · pass at 70% · camera required)
Final proctored exam — the exam screen locks immediately if you switch tabs, exit fullscreen, or lose window focus. Passing it (plus finishing every lesson) is what unlocks your certificate.
Enroll to unlock the exam.
Hands-on labs for this course
Practice what you learn in real lab environments — book access any time.
Apache Kafka
Kafka cluster with sample topics for event-streaming practice.
Snowflake
Snowflake account with sample databases for warehousing practice.
.NET Development Environment
.NET SDK workspace for building ASP.NET Core applications.
Apache Airflow
Airflow instance for building and scheduling data pipelines (DAGs).
Learning materials
Downloadable PDF references for this course, organized by topic.
Data Engineering Quick Reference Guide
Data Engineering · 2 KB