Data Science and Machine Learning, MSc

This English-language master's programme focuses on data science research with a strong machine-learning orientation, especially in life sciences, natural sciences, and medicine. Students develop methodological, technical, and interdisciplinary skills and can specialise in three subject areas.

Academic requirements

Admission is based on a ranking order evaluating the grade average of the Bachelor's degree and additional qualifications (relevant professional or scientific activity).

Minimum GPA

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

English requirements

Applicants whose mother tongue is not English must provide proof of English proficiency. The official programme page accepts an English-taught bachelor's degree or certified English proof at CEFR B2 level, not older than 2 years; proof at C1 or higher may be up to 6 years old. The admissions committee may accept other evidence of sufficient English.

German requirements

Knowledge of German is not necessary for admission.

Accepted bachelor fields

Applicants need a bachelor's degree of at least 180 ECTS in data science, mathematics, statistics, physics, computer science, business informatics, or a closely related field. They must prove 30 ECTS in mathematics and computer science, including 20 ECTS in mathematics and 10 ECTS in computer science, and applicants from other fields must additionally prove 15 ECTS in data science or equivalent work experience.

APS Certificate

Yes

Thesis required

Yes

Tuition fee

None

Semester contribution

435.80 EUR per semester for regular students / international degree students in summer semester 2026.

ECTS credits

120 CP total

Funding info

Tuition: None Semester contribution: 435.80 EUR per semester for regular students / international degree students in summer semester 2026.

Graduates will be excellently qualified for specialist and management positions in various fields of activity involving the collection, management, processing, analysis and interpretation of digital data, as well as for academic research.

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