Industrial Mathematics and Data Analysis (MSc)

Application-oriented MSc in Industrial Mathematics and Data Analysis that combines advanced mathematical modelling, optimisation, data analysis, machine learning, and a year-long project with external research or industry partners.

Academic requirements

Letter of motivation in English explaining interest in industrial mathematics and data analysis and in the University of Bremen, and mentioning the planned choice of a minor subject.

Minimum GPA

No fixed minimum GPA is published on the current official programme page.

English requirements

The reviewed official application page states that applicants need an international English certificate at B2 level. The reviewed official University of Bremen master's portal lists English at B1 for enrolment, but the programme-specific application page requires B2; I used the programme-specific page as the more specific current source. The reviewed official University of Bremen language-certificates page lists examples including TOEFL iBT 72-94 or IELTS 5.5-6.5 for B2.

German requirements

Proof of German language proficiency at an A1 level (CEFR) or higher must be provided.

Accepted bachelor fields

The reviewed official application page states that applicants need a university degree in mathematics or a closely related discipline with at least 180 credit points. It further states that applicants need at least 90 credit points in mathematics acquired during their undergraduate studies.

APS Certificate

Yes

Thesis required

Yes

Tuition fee

None

Semester contribution

EUR 425.40 per semester (current semester contribution published by the University of Bremen for Winter Semester 2025/26).

ECTS credits

120

Funding info

Tuition: None Semester contribution: EUR 425.40 per semester (current semester contribution published by the University of Bremen for Winter Semester 2025/26).

The reviewed official programme page states that graduates work in finance, insurance, engineering, logistics, climate and environmental modelling, data science, and other mathematically intensive research and industry fields, and may continue to doctoral study.

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