Learning paths
Learn it in order
Structured, self-paced series, each one a set of articles meant to be read start to finish.
5 parts
Building Agentic AI Systems
A practical path from why agents matter to building your first multi-agent pipeline: the landscape, the key design decisions, and the hands-on build.
1 part
The Practical Data Analyst
The learning path that matches what employers actually ask for. SQL appears in over 80% of data analyst job postings, Excel in 60%, Python in 50%. This is the order to learn them in, why that order works, and the trap that costs most beginners a year.
3 parts
Data Thinking Series
Learn how experienced Data Scientists think before they write code. The mindset behind data analysis, machine learning and AI: instead of memorising Python, SQL or Pandas commands, you learn how professionals ask questions, understand business problems, validate data, and make better decisions in the age of AI.
3 parts
Building AI in Africa
Applied AI for African markets: the compute and capital realities, what sovereignty actually means, the local-data moat, and case studies across agriculture, fintech and public services. Built for practitioners solving real problems on real budgets.
1 part
From Notebook to Production
The engineering path from a model that works in a notebook to a system that runs reliably in production: deployment, monitoring, cost control, model routing, and the decisions that keep AI alive after the demo.