Curriculum ADS&AI (2025–2026)

The Applied Data Science & Artificial Intelligence curriculum guides students through a four-year progression from foundational skills to professional independence. In the first two years, all students follow the same path, working on real-world projects sourced from external clients—projects are scoped and structured by faculty to support guided learning for larger groups. In years three and four, students take the lead: they work in smaller teams, define the problem themselves in consultation with the client, and deliver end-to-end solutions. This growing autonomy prepares students to operate confidently in real-world settings as data professionals.

Year 1

Block A (15 ECTS) — Introduction to Data Science

• Introduction to Programming with Python
• Foundational Mathematics
• Introduction to AI
• Introduction to Data Science

Block B (15 ECTS) — Machine Learning

• Introduction to Machine Learning
• Mathematical Foundations for Machine Learning
• Introduction to SQL

Block C (15 ECTS) — Deep Learning

• Deep Learning for Image Classification
• Business Understanding
• Responsible & Explainable AI
• Human-Centered AI

Block D (15 ECTS) — Data Science Lifecycle Management

• Time Series Analysis
• Data Engineering I
• Advanced Programming Concepts I
• Project Management I

Year 2

Block A (15 ECTS) — Natural Language Processing

• Natural Language Processing

Block B (15 ECTS) — Computer Vision & Robotics

• Computer Vision
• Robotics and Reinforcement Learning

Block C (15 ECTS) — Research Methodology & Statistics

• Research Methodology
• Statistics

Block D (15 ECTS) — Deploying Machine Learning Solutions

• MLOps and Cloud Engineering
• Data Engineering II
• Advanced Programming Concepts II

Year 3

Semester 5 (30 ECTS) — Specialisation Project

• Specialisation I

Semester 6 (30 ECTS) — Choose Your Path

Choose from:

• Specialisation II
• Internship
• Exchange Abroad
• Minor

Year 4

Semester 7 (30 ECTS) — Choose Your Path

Choose from:

• Internship
• Exchange Abroad
• Minor
• Pre-Master

Semester 8 (30 ECTS) — Graduation Project

• Graduation Project