Decision-making guide: An overview of ETH’s continuing education programmes in data science, machine learning and AI
Get a quick overview of our continuing education programmes in data science, machine learning and artificial intelligence. This comparison highlights the key differences between the programmes and helps you find the right continuing education for you.
Are you planning to further your education in data science, machine learning or artificial intelligence – but are still unsure which programme is best suited to you?
The School for Continuing Education (SCE) has put together an overview for you. Here you can see at a glance the key features of our continuing education programmes in these fields. To help you find your way around even more easily.
You can also download the overview as a Download PDF (PDF, 103 KB).
CAS ETH in AI, Data and Machine Learning
- Technical programme that builds foundations in AI/ML for management decisions and business/IT collaboration;
- Looks at programming, information, data & computers, data science & machine learning as wells as AI and IT in industry;
- Is part of the MAS ETH in AI and Digital Technology.
- Make better data‑driven management decisions by understanding the potential and limits of data, analytics and ML/AI models.
- Collaborate effectively with IT/Data teams, bridging business goals and technical constraints across AI/ML initiatives.
- Apply core DS/ML concepts hands‑on (programming, data handling, model basics) to evaluate use cases and risks in industry contexts.
CAS ETH in Applied Machine Learning and Information Processing
- Technical programme that enables managers to drive digitalisation strategies; includes ethics in ML and digitalisation;
- Looks at programming, data science fundamentals, ML, computer vision, reinforcement learning as well as ethics in ML and digitalisation;
- Is part of MAS ETH in Applied Technology.
- Implement and reason about applied ML workflows (from Python foundations to computer vision and reinforcement learning) for real use cases.
- Lead digitalisation initiatives more confidently, using up‑to‑date insights on ML/AI technologies and their implications.
- Address ethical and communication aspects in AI projects, enabling informed decisions and stakeholder alignment.
CAS ETH in Machine Learning in Finance and Insurance
- Technical programme that focuses on details of machine learning and how it is applied and can provide value in the finance and insurance industry;
- Looks at foundations of ML, industry-relevant applications, responsible use of ML and AI, the innovation process in a fintech environment.
- Design responsible ML solutions for finance/insurance, understanding model fundamentals and the system landscape in which models operate.
- Translate business challenges into ML cases and deliver tangible prototypes through an innovation project with mentoring.
- Critically assess AI/ML risks and ethics in regulated financial services and communicate findings to senior stakeholders.
DAS ETH in Data Science
- Full data science curriculum covering all relevant aspects: ML foundations, algorithms, statistics, big data systems, but also hardware, electronics, clusters and networks;
- Modular programme; three foundations (at least 6 ECTS) and six specialisation tracks available (at least 12 ECTS) & capstone project required (8 ECTS).
- Master the end‑to‑end data science stack, from data management and big‑data systems to algorithms, statistics and machine learning.
- Select and complete a specialisation track and deliver a capstone project that applies state‑of‑the‑art methods to real datasets.
- Integrate technical depth with societal, legal and ethical considerations in data‑driven solutions.
CAS/DAS ETH in Applied Statistical Data Science
- Deep dive into statistical and data analytics methods and their practical application in a software package;
- Also covers machine learning methods;
- Modular programme: CAS covers the fundamentals, DAS offers a variety of advanced modules you can choose from;
- The mandatory DAS parts include a practical workshop (1 ECTS) and a thesis part (2 ECTS);
- Takes place on Mondays.
- Apply core statistical methods and modern ML techniques rigorously to real‑world data using R.
- Choose targeted advanced modules (DAS) and complete a thesis/workshop, demonstrating domain‑relevant analytical expertise.
- Communicate statistically sound results and support evidence‑based decisions in research, development and consulting settings.
You can find detailed information about each programme on the relevant programme website:
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