Minor Digital Science
Dr. Rauchegger offers courses for the Minor Digital Science, which is coordinated by the Digital Science Center.
The Minor Digital Science has an interdisciplinary orientation and can be taken by students of all faculties as a supplement to their studies. Students can either take the entire package (30 ECTS credits) or individual courses.
The courses teach basic digital methods (programming, data analysis, and data management) for students with no prior knowledge of the subject. In addition, value is placed on a critical examination of the achievements of digitization from an ethical, legal and economic perspective.
For more information on the Minor Digital Science, please visit the Digital Science Center website. The courses can be found in the course catalog under elective packages (supplements). If you have any questions, you can contact Dr.Chimiak-Opoka, the teaching coordinator of this minor.
Duration
3 semesters (recommended)
30 ECTS credits
Degree
Annotation on the notice ("Bescheid")
or confirmation document
Prerequisites
None
No enrollment required
References
- The Minor Digital Science is one of the minors offered by our university (just for quick access; the formal description was announced in the university bulletin [Mitteilungsblatt] on July 3, 2019, Issue 71, No. 624 and last updated on July 2, 2021, Issue 93, No. 903. The legally binding version is in German, but there is also a courtesy translation in English).
- The list of the current and upcoming courses given by the DiSC teachers can be found in the course catalog, our books can be accessed via DiSCDown.
- For study-related issues, please subscribe to DiSC@OLAT, which was created as an information exchange platform for students interested in courses related to digitalization. There, we provide a list of currently open and planned courses, suggested studying order, news, a discussion forum, and more.
- A comprehensive article (in German) about the launch of the Minor Digital Science was published in January 2020. Additionally, check the press section for more articles about teaching.
- Check out our testimonials page to learn what other students say about our minor.
Goals
The Minor Digital Science is designed to augment the students’ skill set and open the door to new career opportunities in the age of digitalization. It provides a solid foundation in digitalization relevant to students’ disciplines. It conveys the generic skills required for automated data analysis and offers a selection of courses on specific technologies, methods, and aspects related to students’ majors. Finally, it offers the opportunity to conduct a complete data-based decision-making process under the individual supervision of our teachers. To assure high effectiveness of learning, interdisciplinary groups are kept small and vary from 20 to 40 students.
Expected learning outcome: Upon successful completion of the Minor Digital Science, students are capable of applying fundamental methods in the field of digital science. They understand methods and techniques from the fields of programming, data management, and data analysis. They are aware of the broad context of digitalization. They are able to apply techniques of digital science to their field.
Admission
There are two options to take courses:
- as the complete package of 30 ECTS credits if a major allows for a minor to be incorporated into the curriculum,
- as individual courses (5 ECTS credits each) within the module Non-disciplinary/Interdisciplinary Skills and the module Individual Choice of Specialization.
It is possible to initially take individual courses and have them accredited later if and when a student's major allows for the incorporation of minors. Deans of studies may provide information about future integration plans.
Content
The Minor Digital Science combines information technology skills and mathematical and statistical knowledge all embedded in the substantive expertise from students’ majors.
- Information technology skills are required to manage data, to automate processes, and to make them replicable.
- Mathematical and statistical knowledge is necessary to understand and meaningfully apply statistical models and machine learning methods.
- The substantive expertise is needed to be able to find sources of data and meaningfully interpret results obtained from the analysis of data.
The package consists of five modules with, in total, six courses of 5 ECTS credits each. Half of the courses focus on generic skills (1-3.a) and the other half on domain-specific skills and applications (3.b-5).
FAQ
See OLAT for FAQ