Degree
Master of Science (M.Sc.)
Standard Period of Study
4 semesters
Starting Semester
Winter and summer semester (winter semester recommended)
Teaching Language
English (some elective courses in German)
Admission
Limited admission
Credit Points
120 ECTS

Content

Data scientists know how to extract knowledge from data. They combine skills in Mathematics, Computer Science, Statistics and specific knowledge in an application domain.
In our master’s program Applied Data Science you learn the techniques required to deal with and gain insights from large and often unstructured datasets. In Göttingen, we place special emphasis on the interdisciplinary nature of Data Science. We want to equip you with both in-depth knowledge of the key mathematical, statistical and computer science methods for Data Science as well as make sure you understand how to apply these methods in an application domain. You can choose between Computational Neuroscience, Bioinformatics, Medical Data Science, Digital Humanities and Computational Sustainability, and we are working on expanding the list of possible application domains.

Moreover, we want to teach you how to communicate your insights and reflect on the ethical impacts of collecting and analysing large amounts of data as well as the consequences of automated data-driven decision-making. Our master’s programme is research-oriented and should enable you to carry out scientific research projects autonomously. Beyond research, we also provide the opportunity for internships and interactions with industry partners.

The master's programme Applied Data Science is a graduate programme that requires 120 ECTS (4 semesters) to be successfully completed. The programme includes three areas of study: (1) a core curriculum, (2) the professionalisation section and (3) the master’s thesis. The teaching language is English; only a few non-compulsory elective courses are offered in German.

Core Curriculum

The core curriculum covers important Data Science methods from Computer Science, Mathematics, and Statistics including Data Science infrastructures, statistical modelling, machine learning and several advanced topics as well as ethical aspects of Data Science. You acquire deeper knowledge of Data Science methods in order to develop the professional skills to apply and extend specialised methods of the discipline.

Professionalisation Section

In this part of the programme, you get the opportunity to study tailored to your individual and professional inclinations, as well as your professional ambitions. You will choose one application domain such as Computational Neuroscience, Bioinformatics, Medical Data Science, Digital Humanities and Computational Sustainability (more to come in the near future). For detailed information see application domains.
Furthermore, you carry out a research lab rotation or an industry internship and acquire additional key competencies.

Master’s Thesis

After successfully completing 90 ECTS in the first two areas of study, you write a master’s thesis in order to complete the master's degree.


You can find more details in an interactive insight into the programme structure here (external link):

The module catalogue (Modulverzeichnis) is available here.

The following application domains can be chosen: Computational Neuroscience, Bioinformatics, Medical Data Science, Digital Humanities and Computational Sustainability. We are working on expanding this list.

You can find detailed information here.

Data scientists are currently in high demand in almost all disciplines, both in research and industry. Possible employers can be found throughout all sectors, including the manufacturing industry, banks, insurance companies, the IT sector, consulting, public or industrial research institutes, the public health sector as well as colleges and universities. Excellent graduation also qualifies for PhD programmes.

Access Requirements

A bachelor’s degree with at least 180 ECTS is needed. If the degree has not yet been awarded, a minimum of 135 ECTS is required for application.

At least 60 ECTS have to be successfully completed in basic Data Science methods, e.g. in Data Science, Computer Science, Statistics or Mathematics. This can be fulfilled by different kinds of Bachelor's programmes. If you have covered less than 60 ECTS in Data Science methods, you can compensate up to 15 ECTS through additional courses within your master's program.

Legally binding are the admission requirements described in "Ordnung über die Zugangsvoraussetzungen und über die Zulassung" (see Regulations).

English language proficiency CEFR C1
or
English language proficiency CEFR B2 and German language proficiency equivalent to DSH level 2.

Verification is required. Please find a comprehensive list of the accepted proofs here.

Legally binding are the admission requirements described in "Ordnung über die Zugangsvoraussetzungen und über die Zulassung" (see Regulations).

All non-EU applicants have to pass an online aptitude test, which examines basic knowledge in Mathematics, Statistics and Computer Science. After the application deadline, you will receive an email containing a personal link to the aptitude test and login credentials. The test will take 60 minutes and has to be completed within a few days. The test result will be part of the selection procedure.

Additionally, you will be invited to a personal interview if you are on the shortlist for the programme. The interviews are conducted via video call. The interview will also be part of the selection procedure.

We do not offer any eligibility check. You will find all relevant information in the admission regulations.

Application

Please also check our FAQ Application and Selection Procedure!

For application you have to fill in the provided online application form. To complete the application form, the following documents are required:

  • Graduation Certificate and Transcript of Records (German or English) [PDF, PNG, JPG]
  • Curriculum Vitae (CV) in English or German [PDF]
  • Proof of English proficiency [PDF, PNG, JPG]
  • If required: Proof of German proficiency [PDF, PNG, JPG]
  • A personal statement (up to 300 words) in English or German, addressing your motivation to study Applied Data Science. You should describe due to which skills and personal interests you think you are qualified for the study programme.
  • Aptitude test: All international applicants from non-EU countries are required to pass an online aptitude test, which examines basic knowledge in Mathematics, Statistics and Computer Science. At the end of the application period, you will receive an email containing a personal link to the aptitude test and login credentials. The test will take 60 minutes and has to be completed within a few days. The test result will be part of the selection procedure.

Please prepare all documents before you start with the online application form. Make sure that the documents are properly formatted and scanned in an appropriate resolution. Furthermore, the document's labelling should be self-explanatory.

The entire application process is conducted online only. No hard copies are required. Please do not send any documents by mail unless requested to do so.

Please note: To submit the application form, JavaScript needs to be activated in your web browser.

  • 1 May for applications for the winter semester
  • 1 November of the preceding year for applications for the summer semester
For further information see "Online Application Form"
Applicants will be notified via email. The selection process usually takes a few months. Unfortunately, we are unable to answer questions about the status of your application.
  1. application deadline for the winter semester 2024/25
  2. 1 May 2024
    Application period starts in April

    FAQ Application and Selection Procedure

    Please note: Due to the volume of inquiries, questions already answered in the FAQ will NOT be answered by individual emails.

  1. Contact
  2. Dean of Studies Computer Science

    Georg-August-University Göttingen
    Student Advisory Service
    Goldschmidtstr. 7
    37077 Göttingen

    studienberatung@informatik.uni-goettingen.de