Data Science and Machine Learning, MSc

Program overview

This programme is designed around data science research with a clear emphasis on applications in the life and natural sciences, including medicine. You will build strong methodological foundations for complex data analysis with a particular focus on machine learning. The curriculum trains you to design, implement and evaluate data‑driven algorithms at both technical and conceptual levels, and to communicate results effectively to domain experts.

Students develop interdisciplinary and professional skills to respond to digital transformation challenges within society and academia. The course offers substantial hands‑on experience: a high proportion of guided but independent research takes place directly in the university’s laboratories, preparing you for research projects and applied work.

Specialisations, learning environment and career relevance

You choose one of three specialisations to acquire targeted expertise: Theoretical Foundations of Machine Learning in Mathematics and Natural Sciences; Data Science and Machine Learning in Medicine and Health Care; or Data‑Driven Speech and Hearing Sciences. Teaching is English‑medium and attracts many international students, with small cohorts (around 30 students per year) and an interdisciplinary mix of staff and peers. The programme includes extensive academic support (tutorials, learning workshops) and offers optional integrated language courses and an internship for applied experience.

Graduates are prepared for specialist and management roles that involve collecting, managing, processing, analysing and interpreting digital data, as well as for further academic research. Typical career profiles include data scientist (model development and validation), data analyst (cleaning and preparation), data engineer (data pipelines), and machine learning engineer (adapting and advancing ML/deep learning methods). The programme also fosters industry contacts and start‑up collaboration opportunities.

Key facts & requirements

• Language of instruction: English
• Cohort size: approximately 30 students per year
• Core focus: data science and machine learning for life and natural sciences, including medicine
• Specialisations:
  • Theoretical Foundations of Machine Learning in Mathematics and Natural Sciences
  • Data Science and Machine Learning in Medicine and Health Care
  • Data‑Driven Speech and Hearing Sciences
• Learning format: high share of guided independent research in university labs
• Student profile: interdisciplinary cohort with many international students
• Additional support: tutorials, learning workshops, optional integrated language courses and optional internship
• Career orientation: prepares for roles in industry, healthcare, research institutions and start‑ups; contacts with companies and start‑ups are actively promoted

This MSc is a 120-credit programme combining a method-focused core, a domain specialisation (including a collaborative group project), and an independent Master’s thesis. The curriculum dedicates 42 CP to methodological foundations (30 CP of compulsory core modules plus 12 CP of core electives), 48 CP to one of three specialisation tracks (which includes a 12‑CP group project), and 30 CP for the Master’s thesis. The course mix balances theoretical grounding in statistics and machine learning with hands-on modules, interdisciplinary ethical perspectives, and language or internship options to support international students.

Core compulsory modules build the programme’s methodological backbone and include: Introduction to Data Science, Machine Learning, Applied Deep Learning, Statistical Learning, and an Interdisciplinary Lecture Series on Data Science & Data Ethics. Core electives (12 CP) let you tailor your training with topics such as Trustworthy Machine Learning, Advanced Deep Learning, Time Series Analysis, Explainable AI, IT security, multimodal AI interfaces, research data management, or an internship — plus language support via German or Academic English courses. These choices help you deepen practical skills (model building, evaluation, data pipelines) and critical awareness (ethics, trustworthiness, explainability).

You then select one of three specialisations, each aimed at different application areas and theoretical depth. Theoretical Foundations focuses on mathematical and physical modelling, numerical methods and information processing. The Medicine & Health Care track emphasises medical data pipelines, deep learning for clinical data, big data analytics and clinical decision support. Data-Driven Speech & Hearing Sciences centres on signal processing, acoustics, and speech algorithms. Each specialisation includes compulsory modules and elective options, and all include a group project to develop teamwork and applied research experience. The programme culminates in a 30-CP Master’s thesis demonstrating your ability to carry out independent research or a substantial development project in data science and machine learning.

Requirements / credit breakdown

• Total: 120 CP (ECTS)
• Core (42 CP)
  • Compulsory core (30 CP): Introduction to Data Science; Machine Learning; Applied Deep Learning; Statistical Learning; Interdisciplinary Lecture Series Data Science & Data Ethics
  • Core electives (choose 12 CP from): Exploring Research Data Management; Trustworthy Machine Learning; Machine Learning II; Advanced Topics in Applied Deep Learning; Time Series Analysis; Introduction to IT Security; Designing Explainable Artificial Intelligence; Applied AI – Multimodal-Multisensor Interfaces I; Applied AI – Multimodal-Multisensor Interfaces II; Internship; Current Topics in Data Science and Machine Learning; German or Academic English courses
• Specialisation (48 CP, includes a 12‑CP group project)
  • Theoretical Foundations of Machine Learning in Mathematics and Natural Sciences
    • Compulsory (18 CP): Theoretical Foundations of Machine Learning and Data Science; Group Project
    • Electives (choose 18 CP + additional 12 CP may be taken from the core area): Mathematical Foundations of Statistical Learning; Introduction to Numerical Methods for PDEs; Computational Physics; Modelling of Complex Systems; Current Topics in Theoretical Foundations of Machine Learning in Mathematics and Natural Sciences; Information Processing and Communication
  • Data Science and Machine Learning in Medicine and Health Care
    • Compulsory (30 CP): Medical Data Pipelines; Medical Data Analysis with Deep Learning; Big Data Analytics and Clinical Decision Support; Group Project
    • Electives (choose 18 CP): Special Topics in Medical Informatics; Medical Technology; Medical Basics; Bioinformatics & Omics; Current Topics in Data Science in Medicine and Health Care
  • Data‑Driven Speech and Hearing Sciences
    • Compulsory (30 CP): Digital Signal Processing; Hearing and Communication Acoustics; Algorithms for Speech Processing; Group Project
    • Electives (choose 18 CP): Information Processing and Communication; Introduction to Neurophysics; Processing and Analysis of Biomedical Data; Human–Computer Interaction; Current Topics in Data‑Driven Speech and Hearing Sciences
• Master’s thesis: 30 CP

Learning outcomes (high level)

• Solid methodological foundation in statistics, classical and deep learning methods
• Ability to design, implement and evaluate data pipelines and ML models for large or domain‑specific datasets
• Practical teamwork experience through group projects and optional internship
• Specialisation-specific expertise (mathematical/theoretical methods, medical data and clinical decision support, or speech/hearing signal processing)
• Awareness of ethical, trust and explainability issues in data science and the ability to communicate results in an interdisciplinary context

Graduates are prepared for specialist and management positions and for academic research in fields involving collection, processing, analysis and interpretation of digital data. Typical roles listed by the programme include data scientist (model development and validation), data analyst (data cleaning and preparation), data engineer (data pipeline development and management), and machine learning engineer (selection, adaptation and development of ML/deep learning methods).

Because of the programme's focus on life and natural sciences—especially medicine and health care—graduates are particularly competitive for roles in medical data analysis, clinical decision support and healthcare-oriented AI. The university also promotes contacts with companies and start-ups, supporting transitions into industry and entrepreneurial activities.