Roadmaps
Each roadmap is a sequence of steps towards one role: what to learn, why, and how you know you have got it. Steps covered by the portal link to topics and tasks; the others point to trusted external resources.
- Everyone
AI literacy for everyone
What everyone who uses language models at school or at work needs to know: no programming, with an emphasis on what to trust.
8 steps, 4 covered
- Analyst
Data analyst
From statistical literacy through SQL, Python and BI tools to experiments and data storytelling: the path to a first analytics role.
9 steps, 7 covered
- Scientist
Data scientist
Mathematical and statistical foundations, Python, machine learning and its honest evaluation: the path from analytics to modelling.
9 steps, 5 covered
- AI Engineer
AI engineer
From Python and APIs through how language models work, RAG and agents to evaluation, security and deployment: building applications on top of LLMs.
9 steps, 4 covered
- Agent Engineer
AI agent engineer
From a single model call to agents that use tools, work in multiple steps, and can be measured, secured and operated. Builds on the AI engineer roadmap.
9 steps, 3 covered
- Engineer
Data engineer
Advanced SQL, data modelling, ETL/ELT pipelines, cloud warehouses, data quality and infrastructure: building the foundations that analytics and AI stand on.
10 steps, 4 covered