Data Engineering & ETL Pipelines Professional Certificate

Every dashboard, report and machine learning model depends on data that arrives on time, in the right shape and with known quality. Data engineers build the pipelines that make this happen, and they are among the most sought-after professionals in technology, finance, telecoms, health information systems and humanitarian organisations.

This course teaches the complete pipeline life cycle using free, industry-standard tools. You will extract data from CSV and Excel files, JSON APIs and databases with Python; clean, standardise and reshape it with pandas; write analytical SQL and use DuckDB as a fast local analytical database; design dimensional models with fact and dimension tables and slowly changing dimensions; build data quality tests; make pipelines idempotent and incremental; and understand orchestration with Apache Airflow, transformation with dbt, version control with Git, containers with Docker, and cloud data platforms.

The capstone gives you a realistic problem from the health sector: twelve months of facility reports from five counties in South Sudan, with a mid-year schema change, inconsistent IDs, resubmitted corrections, invalid values and nested JSON stock reports. You will build a tested, re-runnable pipeline into a star schema, load a new month incrementally, and produce trusted indicators on malaria, reporting completeness and medicine stock-outs.

You should be comfortable with basic Python and SQL; our Python and data analytics courses are good preparation. Everything runs in free Google Colab. How the course works: six modules, 23 lessons with more than 35 videos from IBM Technology, freeCodeCamp, Fireship, Seattle Data Guy, Real Python, Alex The Analyst and others, a hands-on activity in every lesson, a quiz after every module, a capstone project and a final certification examination. Most learners finish in 8 to 10 weeks at 4 to 5 hours per week.