Mathematical Data Science (B.Sc.)
Degree: Bachelor of Science (B.Sc.)
Standard duration of study: 6 semesters
Start: Winter semester
Extramural studies possible
- 1st subject semester: limited admission (application to the University)
- 2nd to 6th subject semester: limited admission (application to the University)
- International applicants (non-EU): limited admission (application to International Student Services)
The degree programme
In the Mathematical Data Science degree programme, students learn mathematics, statistics and computer science. They acquire a thorough understanding of mathematics and its methods of analysing data and recognising their underlying structures.
Structure of degree programme
During the first year of the programme, students will acquire sound mathematical knowledge in the areas of analysis, linear algebra and computer science. In the second year, the basic principles of mathematical data science will be covered; statistical and numerical methods are to be dealt with in particular. In the third year, the students will be familiarized with current research topics in a study focus area of their own choice. They will write their Bachelor’s thesis in this research focus area. Possible study focus areas are:
- Optimization and Imaging
- Mathematical Statistics
- Machine Learning
- Applied Statistics and Econometrics
What all of these areas have in common is their requirement for competent scientific work with algorithms in areas such as data analysis, pattern recognition and visualisation.
Graduates of the Mathematical Data Science Bachelor’s degree programme are well prepared to meet either the requirements of a subsequent Master’s degree programme, or the challenges of an entry-level position in core areas of the digital era, due to their abilities for abstraction and identification of patterns, as well as their capacity for conceptional, analytical and logical thinking.
What should their interests be?
Prospective students of this programme should be interested in and have a liking for mathematics and computer science, as well as for finding answers to complex, abstract questions.