Data Science - From Mathematical Foundations to Applications
Master in Data Science
| Enrolment | Programme enrolment |
| Start | Winter semester 2020/21 |
| Duration / ECTS-Credits | 4 semester (PT*) / 90 ECTS-Credits |
| Cost** |
7,000.- EUR (exclusiv ÖH-fee - currently 20.20 EUR per semester) |
| Degree | Master in Data Science (MDS) |
| ISCED-F | 0588 Natural sciences, mathematics and statistics, inter-disciplinary programmes |
| Study Code | UC 992 203 |
| More Information |
Director of the Continuing Education Programme
Department of Mathematics
Administration
Sandra SteixnerDepartment of Mathematics
Technikerstraße 13
6020 Innsbruck
Tel.: +43 512 507-53803
ulg-ds@uibk.ac.at
Dates
Location:
Campus Technik
Technikerstraße 13
6020 Innsbruck
Schedule and dates are found on the website of the Department.
Target Group
The Continuing Education Programme is aimed at persons who are interested in analytic questions and want to become data handling experts. Motivation and background can be multi-layered, from natural sciences, engineering to economics. In any case, basic affinity for mathematics and IT are required.
Admission Requirements
Precondition for being admitted to the continuing education programme is the completion of a pertinent Diploma, Bachelor’s or Master’s Programme at an approved post-secondary educational institution home or abroad, whereby a Bachelor’s programme must correspond to a minimum of 180 ECTS-Credits.
A Diploma, Bachelor’s or Master’s programme in an engineering or natural science subject completed at the University of Innsbruck is in any case a pertinent study programme.
Modules
- Foundations of Data Science
- Methods of Data Science
- Applications in Data Science
Qualification Profile
Data scientists are data handling experts. They have high-level skills for solving complex data-related problems.
Graduates of the Continuing Education Programme “Data Science – From Mathematical Foundations to Applications” have a thorough understanding of the mathematical foundations of Data Science and an overview of state-of-the-art methods for supervised and unsupervised learning.
Among the acquired skills are the implementation of data science tasks with suitable software systems, but also the clear communication of the results of a data science project to experts in the field but also to end users.
*Part-Time
**payment and cancellation conditions
Information on individual courses of the university education courses can be found in the course catalogue.
Participation in a continuing education course requires admission as non-degree student..