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Master's programme

Data Science University of Augsburg

Duration
4 semesters
Language of instruction
English
Tuition fees
No tuition fees
Next application deadline
View details
Language level required
English: B2
German: None

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Programme overview

Quick facts

Degree
MSc
Duration
4 semesters
Full-time / part-time
Full-time
Intake
Winter and summer semester
Joint degree / double degree programme
No
A Diploma supplement will be issued
Yes
Combined Master's degree / PhD programme
No

Language of instruction

English

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.

Programme description

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.

As the programme includes compulsory courses in mathematics and computer science, we expect candidates to have a strong background in both areas.

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 

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.

Programme structure

The programme consists of the following course categories (called module groups)

  • Core Data Science Methods (32 credits)
    • Algorithms and Data Engineering
    • Machine Learning
    • Mathematical Foundations
  • Advanced Data Science Methods / Elective Courses (at least 36 credits)
    • Broad range of elective modules in mathematics and/or computer science, e.g. Quantum Algorithms, Statistics, Optimisation, Modelling, Deep Learning, etc.
  • Scientific Project Work
    • Data Science Project (10 credits)
    • Seminar: Mathematics and Computer Science (4 credits each)
    • Social Aspects of Data Science / Ethics (4 credits)
  • Master's Thesis (30 credits)

Tips on how to organise your studies can be found in our FAQs: https://www.uni-augsburg.de/en/fakultaet/fai/informatik/studienangebot/msc-dsc/#faq_contacts.

Programme structure

Mode of study

  • Fully on-site

International elements in the curriculum

The curriculum of this study programme includes international elements that support global perspectives and cross-cultural learning:

  • Integrated/optional study abroad unit(s)
    It is possible to study abroad in another EU country, e.g. as part of the ERASMUS exchange programme (with funding opportunities). Click here for a list of partner universities. It is possible to study abroad in multiple partner universities worldwide, e.g. as part of the WeltWeit exchange programme.

  • The UniA Language Centre provides excellent opportunities for learning many different languages (not mandatory in the curriculum).
  • The International Office offers intercultural events, language buddies, intercultural buddies and many more intercultural experiences.
  • This programme is taught in English and is intended for students from all over the world with backgrounds in computer science, mathematics or related fields. We are committed to make studies affordable for international students: there are no tuition fees at the University of Augsburg for any student. We embrace cultural diversity. Our excellent student counselling services and the International Office provide individual support for students from all cultural backgrounds.

Application and admission

Application period(s)

The programme can be begun in both winter and summer semesters; the regular deadlines are 1 September and 1 March.

Due to visa processing times, we strongly encourage non-EU citizens to apply well ahead of the deadline (suggested: 1 May for winter, 1 December for summer). In this case, early admission is granted to allow sufficient time for visa processing.

For more information please see our FAQ at https://www.uni-augsburg.de/en/fakultaet/fai/informatik/studienangebot/msc-dsc/#faq_contacts.

Academic admissions requirements

Admission is possible for students with a (nearly completed) Bachelor's degree in Mathematics, Computer Science and related areas. Applicants undergo an aptitude test. More specifically, we expect students to show proof of sufficient previous knowledge in the following areas:  

  • Multivariate Calculus/Analysis (at least 5 ECTS CP)
  • Linear Algebra (at least 5 ECTS CP) 
  • Fundamentals of (Higher) Programming Languages (at least 4 ECTS CP) 
  • Practical Programming Experience (at least 4 ECTS CP), e.g. by practical exercises or a lab course 
  • Algorithms or Numerical Methods (at least 5 ECTS CP) 
  • Discrete Structures/Mathematics, Databases or Data Engineering (at least 5 ECTS CP) and
  • Data Science or Machine Learning (at least 4 ECTS CP)

For details please also see our FAQ at https://www.uni-augsburg.de/en/fakultaet/fai/informatik/studienangebot/msc-dsc/#faq_contacts.

Language requirements

Please check all requirements directly with the respective higher education institution. Exceptions may apply. See our website for further details.

German language requirements

German: None

English language requirements

English: B2

Accepted certificates include
Certificates Minimum result
TOEFL iBT (since 2026)
Total: 4 / 6
Reading: 4 / 6
Listening: 4 / 6
Writing: 4 / 6
Speaking: 4 / 6
Cambridge English Qualifications
Total: B2 First
TOEIC
Total: 785 / 990
Reading: 385 / 495
Listening: 400 / 495
Writing: 150 / 200
Speaking: 160 / 200
UNIcert® English
Total: UNIcert® II
IELTS Academic
Total: 5.5 / 9
TOEFL iBT (before 2026)
Total: 72 / 120
Reading: 18 / 30
Listening: 17 / 30
Writing: 17 / 30
Speaking: 20 / 30

How to apply?

Website

Please check this website.

Click here

Costs and funding

Tuition fees per semester

No tuition fees

Semester contribution

Administration fee of approx. 177.25 EUR per semester

This encompasses the contribution to the student union (responsible for cafeterias, housing opportunities and more student services) as well as the semester ticket (for public transport).

Scholarships and financing information

For possible scholarship opportunities, please consult https://www.uni-augsburg.de/en/studium/studieren-augsburg/stipendien/. Please note that in the German university system, grants are less commonly available than in some other countries. In particular, it is unlikely that you will be able to obtain a scholarship before beginning your studies. If you are considering applying for a scholarship after your first semester, please refer to the open scholarship announcements from the International Office.

Living & services

Support for international students

  • Welcome event
  • Buddy programme
  • Specialist counselling
  • Visa matters
  • Help with finding accommodation

  • A welcome event will be hosted by the International Office shortly after admission.
  • A buddy programme for intercultural exchange is organised by the International Office.
  • Counselling is provided by the Student Counselling Services.
  • You can receive support on visa matters from the Academic Immigration Office.
  • The International Office provides tips for finding accommodation.
  • In particular for new students or students after admission we offer individual support via our onboarding team onboarding@math.uni-augsburg.de.

Contact

Main contact

Institute of Computer Science and Institute of Mathematics

Name
Prof Dr Peter Fischer
Address
86159 Augsburg
Email
E-mail

Additional contact

Name
Birgit Kirchner
Email
E-mail

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Always know what comes next

Study Guide gives you a personal to-do list tailored to your background, qualifications and interests, so you always know what to do next.