M.Sc. Applied Data Science & AI (ADSA) – curriculum
4 semesters: foundations, data & ethics, intelligent systems / career exploration, Master thesis.
The Master's covers the full data journey: understand, collect, clean, visualise, analyse and resolve – through case studies, data engineering, data management, analytics, visualisation, and data storytelling & communication.
Semester 1 – Foundations of the Data Science Universe
- Block 1 – First Steps into Data Science: data science pipeline, data visualisation, storytelling and communication
- Block 2 – Python for Data Science: Python basics, Pandas and NumPy, cleaning and exploring data, reusable code
- Block 3 – Data Engineering: data infrastructure, distributed storage and processing, big data databases
- Block 4 – Specialisation (choose one): Python Engineering for AI (OOP, modules, production-oriented applications) or Recommender Systems & Personalization
- Block 5 – Statistics & Machine Learning: probability, statistical analysis and ML, time series, model evaluation
Semester 2 – Diving Deeper into Data and Ethics
- Block 6 – Data Management: data profiling and quality, data cleaning, schema design
- Block 7 – Specialisation: Collaborative Industry Project I (real business datasets) or LLM Applications, RAGs and Knowledge Systems (prompting, embeddings, vector databases, knowledge graphs, GraphRAG)
- Block 8 – Natural Language Processing: text preprocessing, deep learning for NLP, advanced NLP
- Block 9 – Specialisation: Collaborative Industry Project II or Generative AI and Agentic AI (multimodal AI, AI agents and multi-agent systems)
- Block 10 – Responsible AI & Data Ethics: ethical principles, bias and fairness, designing responsible AI
Semester 3 – Intelligent Systems & Scalable Solutions / Career Exploration
- Block 1 – Deep Learning: frameworks, CNNs/RNNs, GANs/VAEs/GNNs
- Block 2 – Specialisation: AI Vision (image analysis, computer vision) or Reinforcement Learning
- Block 3 – Cloud Computing & Architecture: streaming and real-time systems, cloud architecture, app development
- Block 4 – Entrepreneurship: startup ideas, innovation basics
- Block 5 – Data Visualisation & Storytelling: storytelling frameworks, Tableau / Power BI, immersive storytelling
- Alternative semester: a semester abroad at an SRH partner university, or a full-time internship, instead of regular courses
Semester 4 – Master Thesis
- Block 6 – Master Thesis Seminar: research skills, topic planning
- Block 8 – Master Thesis: independent project, final submission
Career paths
Data Scientist · AI Engineer · Data Analyst · Data Engineer · Data Architect · Machine Learning Engineer · Data Steward · Business Intelligence Analyst · NLP Engineer / Language Model Expert · Quantitative Analyst · AI Consultant · Data Warehouse Architect · Cloud Data Engineer