Data, Science and AI
University of Bayreuth · Bayreuth, Germany
- Level
- Bachelor's
- Award
- Bachelor of Arts Bachelor of Science
- Language
- English
- Duration
- 6 semesters
- Study mode
- full-time part-time (study alongside work)
- Campus
- Bayreuth
- Apply by
- Register by 15 July 2027
- Starts
- Winter semester
University of Bayreuth does not publish a fee for this programme on its own. Its published rate for bachelor's programmes is €0 a year for international students.
Entry requirements and how to apply
- Entry requirements
- Admission requirements: University entrance qualification (German: "Hochschulzugangsberechtigung") TestAS or SAT certificate with a sufficiently high score Recommended skills and interests: Good mathematical preparation Interest in working with computers Problem-solving skills Strong motivation and scientific curiosity
- English language requirement
- B2 required, please provide an official language certificate, e.g.: IELTS Academic: 5.5
- German language requirement
- A1 required/recommended, no proof required
- Application period
- The following is valid for applicants from: all countries For the winter semester: 1 March - 15 July
- Apply through
- Please check this website .
About this programme
Computational methods, data-driven approaches and artificial intelligence (AI) are fundamentally transforming science, technology, industry and society. Since it is clear that the integration of computational methods and AI will continue to drive advancements across disciplines, there is an increasing demand for professionals who not only possess strong technical skills but also have a robust understanding of the theoretical principles that underpin these technologies. This is the unique feature of the interdisciplinary "Data, Science and AI" Bachelor's programme at the University of Bayreuth, whose curriculum blends mathematics, computer and data science with domain-specific knowledge and equips students with the ability to analyse complex data sets, develop algorithms and apply machine learning techniques to real-world problems.
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