Master of Science in Data Engineering

Overview This two-year Master of Science in Data Engineering, taught in English in Bremen, Germany, trains students in the technologies and methods behind modern big-data systems. The curriculum bridges data analytics and data science with the engineering side of data — acquisition, curation, storage and management — so you learn to handle the full lifecycle of large-scale data. Emphasis is placed on building both strong computational skills and the mathematical foundations needed for rigorous analysis, combined with applied experience in selected domains.

Who it’s for and how it’s taught The programme is designed for graduates who want to deepen their technical expertise and pursue research-oriented careers or continue to a PhD. Instruction includes lectures and tutorials, laboratory work, and hands-on project courses; selected modules also offer close collaboration with industry professionals so you gain practical, workplace-relevant experience alongside academic training.

Admission essentials (concise)

• Duration: 2 years (Master’s level)
• Language of instruction: English
• Required prior degree: Bachelor’s degree in computer science, physics, applied mathematics, statistics, electrical engineering, communications engineering, or a closely related discipline
• Intended outcome: preparation for research-oriented Master’s work, industry roles in data engineering/big data, or progression to a PhD program

This master's curriculum is built around a strong Methods/core component that equips students with the technical foundations needed for modern data engineering. Core modules cover practical and theoretical topics such as handling large-scale data (The Big Data Challenge), extracting insights (Data Analytics, Machine Learning), and ensuring safe, lawful treatment of data (Data Security & Privacy, IT Law). Hands-on technical courses — Data Acquisition Technologies and Sensor Networks, Image Processing for Data Engineers, and Introduction to Data Management with Python — ensure you gain concrete experience building data pipelines, processing sensor and image data, and managing datasets programmatically.

Beyond the core, the program offers an Elective area so you can deepen expertise by choosing one of four specialization tracks: Computer Sciences, Geoinformatics, Bioinformatics, or Business and Supply Chain Engineering. The Discovery area builds research and applied-project experience: a project seminar on current data-engineering challenges is followed by two advanced data-engineering projects, giving you a portfolio of work. The Career area rounds out technical training with employability-focused modules in language, communication and ethics, and career skills specifically tailored to data engineers — preparing you to move into industry roles with both technical and professional competencies.

Key modules (core / Methods)

• The Big Data Challenge
• Data Analytics
• Data Security & Privacy
• IT Law
• Machine Learning
• Data Acquisition Technologies and Sensor Networks
• Image Processing for Data Engineers
• Introduction to Data Management with Python

Electives and specialization

• Choose modules from one track: Computer Sciences, Geoinformatics, Bioinformatics, or Business and Supply Chain Engineering

Discovery and project work

• Project seminar on contemporary data-engineering topics
• Two advanced projects in data engineering (applied research / implementation)

Career and professional development

• Language skills
• Communications and ethics
• Career skills for data engineers

Typical learning outcomes

• Design and implement scalable data pipelines and storage solutions
• Apply machine learning and analytics techniques to large and heterogeneous datasets
• Process sensor and image data for engineering applications
• Implement security-, privacy- and legally-compliant data solutions
• Communicate technical results effectively and navigate professional and ethical issues in industry contexts

Graduates are prepared for technical and leadership roles that involve designing, building and maintaining data infrastructure — for example, data engineer, data architect, big data specialist or machine learning engineer. The combination of core technical modules, applied projects and internship support equips students to work across industries such as technology, engineering, healthcare, logistics and finance.

For those interested in academic or research careers, the programme provides a solid foundation for doctoral study (PhD) and research-oriented positions, supported by methods training and opportunities for advanced project work.