Python Professional
Production Data Engineering
Lead Data Engineer & Architect
About this course
Python has become the dominant language in data analytics, engineering and science — approachable enough to learn quickly, powerful enough to use in production. SQL handles structured queries; Python handles everything around them.
Course at a glance
Datasets used
| SalesPY | Retail dataset for sales analysis, revenue computation, and customer segmentation work. |
| FinancePY | Banking dataset for transaction monitoring, risk classification, and compliance reconciliation work. |
Tools you'll need
- •Python 3.12+
- •VS Code with Python + Pylance extensions
- •Virtual environments (venv)
- •Git
What you get when you enrol
- ✓Lifetime access to every lesson, exercise, and update — including future revisions to this course.
- ✓12-month Azure SQL practice access against the same datasets used in the course (read-only). Renews on request for active learners.
- ✓Auto-graded labs in your browser — write SQL, hit Run, get instant feedback against the expected result.
- ✓AI-graded Professional Challenges — open-ended scenarios reviewed against a published rubric, not just a single right answer.
- ✓Course discussion + community — talk to other learners and ask the instructor questions inside the course.
- ✓Basic Certificate on demonstrated capability — awarded when you complete every Hands-On Lab and Module Readiness Check, plus the Professional Challenges. Confirms you can write, run, and defend course-level SQL against real datasets.
- ✓Optional Advanced Certificate on completion of SQL Core multi-project— a separate credential awarded when you complete all three capstone projects, each independently assessed and approved by an instructor. Each project is end-to-end query engineering against a real brief with defined acceptance criteria — proves competence at a level an employer can actually evaluate. The Basic Certificate alone confirms course mastery; the Advanced Certificate confirms you can deliver.
- ✓Optional live training upgrade — instructor-led cohort sessions with capped capacity, sold separately.
What you'll learn
- ✓Build production data pipelines with error handling and logging
- ✓Create REST APIs with FastAPI or Flask
- ✓Write unit tests with pytest and implement CI/CD patterns
- ✓Work with databases using SQLAlchemy ORM
- ✓Package and deploy Python applications
- ✓Complete a production data engineering project
Who this is for
Curriculum
117 lessons · 33h 56m1. From Applied to Professional5 lessons
2. OOP Design Patterns10 lessons
3. Decorators & Context Managers10 lessons
4. Generators & Iterators10 lessons
5. The Type System11 lessons
6. Async I/O9 lessons
7. Multiprocessing & Concurrency9 lessons
8. Packaging & Dependency Management11 lessons
9. CLI Tools & Configuration9 lessons
10. Docker & Containerisation9 lessons
11. CI/CD Pipelines11 lessons
12. Production Patterns10 lessons
13. Capstone Project — EnergyPY Professional: Production Billing Service2 lessons
14. Python Professional Cheat Sheet1 lessons
Prerequisites
- •Python Applied or strong intermediate Python skills
Pair this course with the Python Professional portfolio
The Python Professionalportfolio mirrors this course level — once you've worked through the lessons, the projects give you a graded, instructor-reviewed deliverable on the same stack and a LinkedIn-ready summary on completion.
3 sector standalones available — pick one or the full trilogy.
What learners say
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Real-data evidence employers can see.