Four years, four intake moments per year. You take year 1 together as one group; from year 2 onwards you choose one of two profiles. Below you'll find all modules per year of study, with what you learn in each.
You take the first year together with the whole group. You discover where AI adds value, build your first bots and lay the technical foundation.
AI is about creating value for the organisation. You learn to determine what value AI can deliver, what the organisation wants to achieve and where it should invest.
Read more → NovemberYou build a first solution and immediately experience what it's like to have your own 'bot' — for yourself and perhaps already for your organisation.
Read more → FebruaryThe full breadth of the ICT field along five pillars: ICT organisation and roles, software and databases, hardware and infrastructure, security, and networks.
Read more → AprilAI solutions have to work together with the processes and information in your organisation — only then are they deployed as an integral part of it.
Read more →From November you choose: Gen AI Makers (generative AI) or AI Classic Makers (data analysis and classic AI). You take the remaining modules together.
Translating laws and regulations (such as the AI Act and GDPR), governance and business requirements into responsible, workable AI applications.
Read more → NovemberGetting the most out of generative models: advanced prompting, context design and retrieval — reproducible and testable.
Read more → NovemberStatistics and analytical skills for classic AI: exploring data, testing hypotheses and critically assessing model outcomes.
Read more → FebruaryConnecting and unlocking data sources for AI: APIs, data standards and interoperability between systems inside and outside your organisation.
Read more → AprilMeasuring and improving data quality, and setting up the governance that AI requires — and that becomes possible with AI.
Read more →You deepen your profile: agent teams and workflows for Gen AI Makers, data science for AI Classic Makers — and together you design AI-driven services.
Designing data models that support AI applications: from conceptual model to implementation, with an eye for reuse and scalability.
Read more → NovemberMaking multiple AI agents work together in workflows: orchestration, connecting tools and building in human checkpoints where needed.
Read more → NovemberBuilding predictive models with machine learning: from feature engineering to evaluation and explainable interpretation of results.
Read more → February(Re)designing services with AI in the customer journey: from service blueprint to a working prototype that you test with real users.
Read more → AprilReliable decision-making with AI: setting up monitoring, detecting bias and making compliance demonstrable for regulators and customers.
Read more →You learn to manage AI implementations, carry out a major project assignment within your profile and finish with your graduation research.
Keeping AI applications running in production: lifecycle management, monitoring, handling incidents and evolving them in a controlled way.
Read more → NovemberA realistic project assignment: implementing an agent solution with the accompanying data management at a real organisation.
Read more → NovemberA realistic project assignment: implementing a data science solution with the accompanying data management at a real organisation.
Read more → February – AprilYour graduation research in your own organisation or at a client: demonstrate that you apply AI responsibly at professional bachelor level.
Read more →Alongside the modules, you work on four continuous learning strands throughout all years.
Communicating, advising, presenting and collaborating — the skills that make your AI knowledge actually land in your organisation.
Read more → OngoingThe concepts, possibilities and limitations of data and AI — so you can contribute and decide on solid ground, even outside your own specialism.
Read more → OngoingWorking iteratively in multidisciplinary teams: planning, prioritising and delivering in short cycles — the way it's done in practice.
Read more → OngoingEthical considerations around data and AI: fairness, transparency and societal impact — woven into every module and every project.
Read more →You work with the tools organisations actually use today — from generative models to data governance standards.
Tools change with the market — above all, you learn the underlying patterns, so you can quickly pick up the tools of the day after tomorrow as well.
Email or call the programme team — or view the full programmes on hu.nl.