- Degree
- Master of Science in Data Science
- Course location
- Augsburg
- Teaching language
- • English
- Languages
- The language of instruction is English. Some elective courses may be taught in German, but it is possible to complete the programme entirely in English.
- Full-time / part-time
- • full-time
- Programme duration
- 4 semesters
- Beginning
- Winter and summer semester
- Application deadline
- For every winter semester:
• 1 September
• Non-EU applicants: Due to visa issues we strongly recommend to apply well ahead (suggested: 1 May). Early admission will be granted to allow sufficient time for visa processing.
For every summer semester:
• 1 March
• Non-EU applicants: Due to visa issues we strongly recommend to apply well ahead (suggested: 1 December). Early admission will be granted to allow sufficient time for visa processing.
- Tuition fees per semester in EUR
- None
- Combined Master's degree / PhD programme
- No
- Joint degree / double degree programme
- No
- Description/content
- The MSc Data Science offers a comprehensive education in the concepts and methods of data science, incorporating knowledge and methodology from Computer Science and Mathematics. Special emphasis is placed on the sound training of fundamental (mathematical) concepts as to be able to competently assess the properties and limitations of the methods. The main topics are:
•
Machine Learning (ML), AI, and Deep Learning
•
Statistics, Matrix Algorithms, and mathematical foundations of ML
•
Data Structures, Algorithms and Optimisations for Big Data
•
Data Engineering with Big Data Infrastructures and Data Wrangling
As the programme includes compulsory courses in mathematics and computer science, we expect candidates to have a strong background in both areas.
The programme offers a wide range of application areas in collaboration with adjacent departments: Medicine, Economics, Engineering, Geography, Material Science, Physics and others. It provides close connections to industry and research. The field of data science as a modern interdisciplinary science underpins the process of digitalisation and the effective utilisation of data in many application areas.