Master Data Science

Master Data Science

Study Course Coordinator

Prof. Dr. Frauke Liers

Student Advisory

Dr. Daniel Tenbrinck

Please send only questions related to the field of Data Science or the structure of this study course to the student advisory. Questions on the application and admission process have to directed to the Master’s Office at


Why apply for this program?

Data Science has become a revolutionary technology that everyone seems to talk about. It is becoming a key concept for large private businesses, public institutions and research. While it is not easy to define it in a few words, data science deals with the methods and tools needed to analyze data and draw actionable conclusions from the results gained in the process. These methods and tools, which cover big data and their analysis, data modeling, machine learning, and simulation methods, are located mainly at the intersection of three subjects: computer sciences, mathematics, and statistics. Consequently, this new Master’s program at Friedrich-Alexander University is jointly taught by lecturers from these three fields.

This program uses dynamic learning methodologies to ensure our students stand out in today’s competitive job market. Students will enjoy a wide variety of long-lasting benefits:

  • Hands-on teaching methodology.
  • A world-class institution.
  • Career-focused curriculum.

Career prospects

As a data scientist you can work in national and international companies or in the public sector. Some exemplary sectors to find exciting job positions would be:

  • Technology Industry
    Thanks to the various developments in data science, this industry is gradually evolving from an art into a science. Modern cyber security mechanisms heavily rely on data science. Data scientists are bringing probabilistic and statistical methods into the IT industry. Data collected from different disciplines can be instrumental in helping with solutions to cyber security threats.
  • Travel Industry
    Travel personalization has become an increasingly deeper process than it used to be. The possibility to create customer profiles based on segmentation, offering personalized experiences according to their needs and preferences, has its foundations in data science. Forecasting the behavior of travelers by knowing where they want to go next, what kind of prices are they ready to pay, and when to launch special promotions, hugely depends on the level of applying data scientists‘ skills and abilities.
  • Energy Industry
    The energy industry experiences major fluctuations in prices and higher costs of projects. Obtaining high-quality information has not been so important! Data scientists help in cutting costs, reducing risks, optimizing investments and improving equipment maintenance.
  • Pharmaceuticals
    Connected to human health, the pharma industry has also emerged as an industry where data science is increasing its application. For example, a pharmaceutical company can utilize data science to ensure a more stable approach for planning clinical trials. Companies need to resort to data science in order to build precision into their calculations of the potential success or failure of the clinical trials.

Undoubtedly, there are many more sectors that a data scientist can work. Data science careers are in high demand, particularly in Germany, and this trend will not be slowing down any time soon.
After the master’s degree, a scientific career is of course also possible. Definitely, you can then deepen your knowledge and develop various programming tools for scientific codes and data analysis.

Areas of specialization

At the beginning of the master’s degree, one major field of study is selected from the following subject areas as part of an individual study agreement:

  • Data-oriented optimization
  • Mathematical Theory / Basics of Data Science (taught in German)
  • Databases Knowledge Representation (taught partly in German)
  • Machine learning / artificial intelligence
  • Simulation and Numerics
  • Mathematical Statistical Data Analysis (taught in German)

The other subject areas together form the minor field of study. The courses are mainly taught in English. Every student chooses a mentor at the beginning of the study course. The mentor gives the student advice how to design the study plan in accordance with the student’s individual interests.

Structure of the degree programs

The standard time to degree is four semesters (two years). Currently, there is no part-time option for studying M.Sc. Data science. Students must acquire 120 ECTS. The program is structured as follows:

Detailed information regarding modules and study plan can be found under „Important document and further info“ paragraph in the „Module Handbook“ section.

Application subjects

In the following application subjects modules with a total of 15 ECTS should be taken:

  • Chemistry (taught in German)
  • Digital humanities (taught mainly in German)
  • Geography
  • Geosciences
  • International Information Systems
  • Medical data science
  • Material Science
  • Physics (taught mainly in German)

Study course plans

In the following we present three exemplary study course plans as an orientation help assuming the first semester starts in the winter semester.

Example I (Major field: ML/AI, Application subject: Medical Data Science)
Example II (Major field: SN, Application subject: Digital Humanities)
1st semester Application subject: Wearable and Implantable Computing (5 ECTS)

Application subject: Digital Health (5 ECTS)

Major field: Artificial Intelligence I (7,5 ECTS)

Major field: Pattern Recognition (5 ECTS)

Minor field: Convex Geometry and Applications (5 ECTS)

Core module: Mathematics of Learning (5 ECTS)
(total: 32,5 ECTS)

Application subject: DH-Module 2: Society and space (5 ECTS)

Major field: Simulation and Modeling 1 (5 ECTS)

Minor field: Middleware – Cloud Computing  (7,5 ECTS)

Minor field: Artificial Intelligence I  (7,5 ECTS)

Core module: Mathematics of Learning (5 ECTS)
(total: 30 ECTS)

2nd semester Application subject: Computational modelling of cancer network (5 ECTS)

Major field: Artificial Intelligence II (7,5 ECTS)

Minor field: Partial Differential Equations Based Image Processing (5 ECTS)

Minor field: Distributed Databases and Transaction Systems (5 ECTS)

Core module: Deep Learning (5 ECTS)
(total: 27,5 ECTS)

Application subject: DH-Module 1: Language and text (5 ECTS)

Major field: Simulation and Modeling 2 (5 ECTS)

Major field: Partial Differential Equations Based Image Processing (5 ECTS)

Major field: PDEs in Data Science (5 ECTS)

Core module: Deep Learning (5 ECTS)

Technical qualification: Approximate Computing (5 ECTS)
(total: 30 ECTS)

3rd semester Major field: Research Project AI (10 ECTS)

Minor field: Inverse Problems and their Regularization (5 ECTS)

Technical qualification: Nailing your Thesis (5 ECTS)

Core module: Selected Topics in Mathematics of Learning (5 ECTS)

Master’s seminar (5 ECTS)
(total: 30 ECTS)

Application subject: In-depth studies in DH (5 ECTS)

Major field: Numerics of Partial Differential Equations (10 ECTS)

Minor field: Discrete Optimization I (5 ECTS)

Core module: Selected Topics in Mathematics of Learning (5 ECTS)

Master’s seminar (5 ECTS)
(total: 30 ECTS)

4th semester Master’s thesis (30 ECTS)
(total: 30 ECTS)
Master’s thesis (30 ECTS)
(total: 30 ECTS)
Example III (Major field: DO, Application subject: International Information Systems)
1st semester Application subject: Innovation and Leadership (5 ECTS)

Application subject: Process Analytics (5 ECTS)

Major field: Discrete Optimization I (5 ECTS)

Major field: Convex Geometry and Applications or Algorithmic Game Theory or Optimization in Industry and Economy (5 ECTS)

Minor field: Pattern Recognition (5 ECTS) or Numerics of Partial Differential Equations (10 ECTS)

Core module: Mathematics of Learning (5 ECTS)
(total: 30 or 35 ECTS)

2nd semester Application subject: Business Intelligence (5 ECTS)

Major field: Discrete Optimization II (10 ECTS) or Robust Optimization II (5 ECTS)

Minor field: Practical Course: Modelling, Simulation and Optimization (5 ECTS) or Software Applications with KI (10 ECTS)

Core module: Deep Learning (5 ECTS)
(total: 25 or 30 ECTS)

3rd semester Major field: Advanced Nonlinear Optimization (10 ECTS) or Mathematical Foundations of Data Analytics, Neural Networks, and Artificial Intelligence (5 ECTS)

Minor field: Machine Learning for Time Series (5 ECTS)

Technical qualification: Approximate Computing (5 ECTS)

Core module: Selected Topics in Mathematics of Learning (5 ECTS)

Master’s seminar (5 ECTS)
(total: 25 or 30 ECTS)

4th semester Master’s thesis (30 ECTS)
(total: 30 ECTS)


Admission requirements:

  • A completed B.Sc. degree in Mathematics, Industrial Mathematics, Mathematical Economy, Computer Science, or Data Sciences. Furthermore, one is eligible for admission with a completed B.Sc. degree  with a content of at least 60 ECTS in Mathematics and/or Computer Science, e.g., Physics or Engineering.
  • English proficiency at level B2 CEFR (vantage or upper intermediate) or six years of English classes at a German secondary school (Gymnasium). Applicants who have completed their university entrance qualifications or their first degree in English are not required to provide proof of proficiency in English.


The application for M.Sc. Data Science is performed online:

  • Register online using the page
    Note: Applicants who have not yet an „ IdM Account “ have to register at IdM first (on ). IdM stands for Identity Management of FAU. Then, using your IdM account, you can set up your online application.

    For further questions concerning the process of application please contact our Master’s Office:
    For further questions on the online application portal please send an email to:

Application period for the summer intake: Starting in December.
Furthermore read: Guide to the application process.



FAU Erlangen-Nürnberg is not able to offer accommodation. The University does not operate any student accommodation and is not allowed to act as an estate agent. However, information on finding accommodation is provided here.

Financing your studies and costs:

There is only one semester fee / student services fee for each student, no matter which country of origin, that has to be paid every semester. Further information regarding costs of studying such as living or food is provided here.
FAU Erlangen-Nürnberg does not have any funding available to support international students with their living costs. For this reason, international students usually receive scholarships from their home country or use their own funds to finance their studies. You might search for scholarships at the DAAD website here.


After you receive your admission letter you have to enrol for the next semester by sending your certified documents via postal service to the Student Records Office. The enrolment fee should be transferred several days before, especially if transferred from abroad. In this case we recommend to calculate with at least two weeks.

You can find further information on enrolment and first steps afterwards here.

After enrolment you are given access to online teaching resources and several other important platforms. Start by creating a user account at FAU (IdM account) and get familiar with the two online platforms „StudOn“ and „UnivIS„, which will help you to plan and manage your courses.

We will organize a virtual welcome reception for all new enroled students of the M.Sc. Data Science study course via Zoom on Friday, 15th October 2021 at 3pm (German time) to help you with a good start at FAU. Participants will receive an invitation email with the Zoom link before the meeting.

Important documents and further info

  • Guideline on entry from high risk areas.
    Due to the COVID-19 pandemic, restrictions take place for entry into Germany from a large number of countries. Here you can find a guideline on entry requirements and regulations regarding self-isolation
  • Module handbook.
    A preliminary overview of available modules can be downloaded here (German version).
    The module handbook for the next semester can be downloaded here.
  • Module catalogues.
    The module catalogues for all currently available modules can be downloaded here.
  • Examination Regulations.
    The study and examination regulations for the bachelor and master degree in Data Science can be downloaded here .
  • Study plan agreement.
    The Excel sheet containing a study plan agreement can be downloaded here (last updated: 1st October 2021)
  • Information on examination registration portal meinCampus
    Once per semesters all students have to register for their exams after being notified by email. A short overview on the functionality of the examination registration portal meinCampus can be downloaded here.
  • Information for international students.
    You will find further information for international applicants under this link .
    Studying at FAU also means getting to know everyday life in Germany as well as German and Franconian culture. An interesting video of international students talking about their experiences in Germany can be found here .
    Information about the orientation courses for international students offered by various faculties and offices at FAU is provided here .
  • Study at FAU.
    Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) is one of the largest research universities in Germany. Under this page you can find information regarding studies at FAU and joining our family.
  • Labor market for FAU graduates.
    See the QS Graduate Employability Ranking 2019.
    If you want to find a job and internship in Data Science in the area around Nürnberg and Erlangen have a look at the Stellenwerk homepage. For additional offers please send an email to and you will be added to a dedicated mailing list in which occasional job offers are posted.
  • Information on the Coronavirus and its impact on FAU.
    The coronavirus pandemic had an impact on the university life too. In order to keep informed of everything that is going on, we have provided the following links: and .
Friedrich-Alexander-Universität Erlangen-Nürnberg