The digital transformation of our world is creating a constantly growing treasure of very large amounts of often unstructured data. Here, unstructured means that the data is difficult to process by machine because it is not organized. The field of Data Science deals with the development and application of methods to gain insights from such data. For example, medical findings as well as X-ray images are easy for doctors to interpret and it is easy for them to link the information from both data sources to form an overall picture. However, this is still very difficult for intelligent systems that could support doctors in their work. Another example of a Data Science problem is using aerial images to assess how healthy a field is in order to take effective action against plant pests and diseases with as little intervention as possible.

Data Science is located at the intersection of Mathematics, Computer Science, Statistics and Machine Learning. Therefore, students learn the fundamentals of Computer Science and Mathematics as well as in-depth knowledge of data analysis in the Bachelor’s programme »Applied Data Science«. This includes Machine Learning, Statistics, Data Literacy, Data Protection and Privacy, Data Infrastructures and High-Performance Computing.

The Göttingen study programme is characterized in particular by its interdisciplinary profile - not only in the fundamentals. As good data scientists require not only methodological knowledge but also expertise in the field of the data to be processed, students choose an application domain in which they learn to apply the methods of Data Science. As a basis for this, you attend subject-specific courses from the respective areas. You can currently choose between Digital Business Administration, Biology/Bioinformatics, Digital Humanities, Medical Informatics, Breeding Informatics, Physical Modeling and Data Analysis as well as Computational Sustainability. It is possible to visit courses from more than one of the application domains.

Students are also ideally prepared for the often international working environment thanks to a growing range of courses taught in English. With »Machine Learning«, one of the advanced compulsory modules is taught in English. Of course, students have the opportunity to attend an English course as part of their studies if required.

A total of 180 ECTS has to be completed in order to earn the Bachelor's degree.

The programme is divided into three fields of study: »core curriculum«, »area of professionalisation« and the »Bachelor's thesis«.

• Fundamentals of Computer Science:

  What are the basics of automated data processing with computers?

  Basic concepts in Computer Science, programming, databases

• The mathematical foundations of Data Science:

  Which mathematical concepts do I use to analyze data and let computers learn?

  Analysis, Linear Algebra, Probability Theory

• Fundamentals of Data Science:

  How do I gain insights from data?

  Statistics, Data Literacy, Machine Learning

• Elective area »Data Science«: specialization in the field of »Infrastructure and Processes« or in the field of »Data Analysis«.
• »Application domain«: specialization in one of the seven »application domains«. It is possible to gain insights into several »application domains«.
• »Practical project and internship«: Practical work in a team and collaboration in a research group.
• »Key skills«: among others, programming in Python and dealing with the ethical aspects of Data Science. There is also the opportunity to attend language courses or acquire soft skills, e.g. in rhetoric or intercultural competence.

• The »Bachelor's thesis« can be written in one of the »application domains« as well as in the methods of Data Science.
• Prior to the »Bachelor’s thesis« (worth 12 ECTS) students are required to attend a course on scientific writing (worth 3 ECTS) which also includes giving and receiving peer review during the thesis writing process.

Detailed information on the structure of the degree programme, the »application domains« and all the modules can be found in the directory of modules, available at "Regulations" in the right-hand column. Sample study plans can be found in the examination and study regulations.

You can also click through the programme and its contents in detail in our interactive study structure (external link, in German):

In the »Applied Data Science« degree programme, every student has to choose a main application domain. However, there is also the opportunity to "get a taste" of other application domains and earn credits in different fields.

Details on the application domains can be found here. There currently is a total of seven application domains to choose from: Digital Business Administration, Biology/Bioinformatics, Digital Humanities, Medical Informatics, Breeding Informatics, Physical Modeling and Data Analysis as well as Computational Sustainability.

Data scientists are currently being recruited in almost all disciplines, both in research and industry. Potential employers can be found in particular in marketing, banks, insurance and reinsurance companies, in the IT sector, in management consultancies, in public research institutes or development and research departments in companies, in the pharmaceutical sector (clinical studies) as well as in the public health sector, in colleges and universities. Due to the proximity of the programme to the fields of application directly at the university, particularly research departments that deal with data-driven research in the respective domains are potential areas of work. There is currently a lack of specialists in all of these fields in Germany and internationally.

Please refer to: Entry requirements at Göttingen University

The Bachelor's degree programme »Applied Data Science« is a German-language program. Therefore, sufficient knowledge of the German language is required.
Advanced knowledge in English is recommended.

See more at: Language requirements for international prospective students

Those who choose »Applied Data Science« as a field of study should be interested in both a mathematically formal and an application-oriented practical approach.
The ability to work in a team is an important prerequisite for later professional life.
Specialized technical knowledge, especially in programming, is not a prerequisite for this programme.

Open admission (Enrollment without prior application)
Application Guide

Open admission (Enrollment without prior application)
Application Guide

Open admission
(Application at International Studies)

The Computer Science preparatory course (preliminary course) is aimed at first-year students of the Bachelor's degree programmes in »Applied Computer Science«, »Applied Data Science« and the »2-subject Bachelor's Computer Science«. We particularly recommend participation for first-year students who have not done Computer Science at school or have no experience in programming yet. The preliminary course covers topics from Computer Science lessons at school and provides an introduction to the basic use of computers in our computer rooms. The preparatory course is also an ideal networking opportunity to make initial contacts with your fellow students and gain an insight into everyday student life. The Computer Science preparatory course takes place before the start of the lecture period.

Information on content, registration and dates can be found here.

We recommend that all first-year students of our Bachelor's degree programmes take part in the mathematical preparatory course as well as the Computer Science preparatory course. It eases the transition from school to university and offers a good opportunity to get to know your fellow students. The mathematical preparatory course also takes place before the start of the lecture period and does not overlap with the Computer Science preparatory course.

Further information on the mathematical preparatory course can be found here.