Week 2 - Structuring Data Pipelines
Week 3 - Ingesting and Validating Data
Week 4 - Data Processing with Pandas
Week 5 - Docker and CI/CD for Data
This program is designed to take you from the foundations of Python programming into the world of Data Engineering. Over 16 weeks, you will learn how to build, deploy, and orchestrate modern data pipelines using industry-standard tools and cloud platforms. You'll move from simple data manipulation to complex ETL/ELT processes, database modeling, and professional dashboarding. This track is hands-on and intense - you'll be building real systems that process data from end to end.
Data is the fuel of modern organizations, but it's useless if it's messy, disconnected, or slow. Data Engineers are the architects who build the systems that collect, clean, and deliver this data. We chose this track because it's a high-demand field with a focus on building robust, scalable infrastructure. You'll learn to work with Python, SQL, Docker, and Azure - skills that are essential in any modern tech stack. If you enjoy problem-solving at scale and building things that "just work", this is the track for you.
💡
The skills you develop here such as systems thinking, automation, and understanding data lifecycles are highly valuable across the entire tech industry, from software development to specialized data science roles.
Build, read, and understand data pipelines
Apply software engineering principles to build robust, scalable data ingestion and transformation systems.
Use cloud-native data tools effectively
Work confidently with Azure, Docker, dbt, and modern orchestration tools to manage data workflows.
Understand modern data architecture
Explain and use fundamental concepts such as ETL vs ELT, data modeling (Star Schema, OBT), and distributed computing.
Collaborate in a data-driven environment
Work in teams, follow version-control workflows, and communicate technical decisions clearly to both developers and stakeholders.
Work productively and critically with AI tools
Use AI tools to support learning and development, while understanding their limitations, validating outputs, and maintaining code quality.
Ready? Let’s begin with Week 1 - Python Foundations
Found a mistake or have a suggestion? Let us know in the feedback form.