Master Data Science

Student Advisory

studienberatung-datascience@math.fau.de

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 be directed to the Master’s Office at zuv-masterbuero@fau.de

 

News

• You can now generate your study plan online at the new website of the Study Plan Generator (SPLAG).
• We have updated our frequently asked questions (FAQ) document, which can be downloaded here and at the bottom of this page.

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.
• Individual, interest-based 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-based optimization
• Mathematical Theory / Fundamentals of Data Science (taught in German)
• Databases and Knowledge Representation (taught partly in German)
• Machine learning / Artificial intelligence
• Simulation and Numerics
• Mathematical Statistical Data Analysis (taught mainly 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). 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.
Please note that some of the core modules in the Master Data Science are only taught once per year, i.e., not every semester. This applies to both modules „Mathematics of Learning“ as well as „Selected Topics in Mathematics of Learning“, which are only taught annually in the winter semester.

Application subjects

Out of the following application subjects, you should take modules with a total of 15 ECTS. You are free to choose modules mixed from all applications subjects in either English or German, depending on your language skills.

• Artificial Intelligence in Biomedical Imaging
• Chemistry (taught in German)
• Digital Humanities (taught mainly in German)
• Geography
• Geosciences
• International Information Systems
• Material Science
• Medical Data Science
• Multimedia Engineering
• Physics (taught mainly in German)

Study course plans

In the following we present three exemplary study course plans as an orientation help assuming you are studying Data Science as a full-time study (~30 ECTS per semester) and the first semester starts in the winter semester.

	Example I (Major field: ML/AI)	Example II (Major field: SN)
1st semester (winter semester)	Application subject: Wearable and Implantable Computing (5 ECTS) Application subject: Process Analytics (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: Business Intelligence (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 (summer semester)	Application subject: DH-Module 1: Language and text (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: Computational modelling of cancer network (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 (winter 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: Quantum Computing (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 (summer semester)	Master’s thesis (30 ECTS) (total: 30 ECTS)	Master’s thesis (30 ECTS) (total: 30 ECTS)

	Example III (Major field: DO)
1st semester (winter semester)	Application subject: Process Analytics (5 ECTS) Application subject: Digital Health (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 (summer semester)	Application subject: DH-Module 2: Society and space (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 (winter 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 (summer semester)	Master’s thesis (30 ECTS) (total: 30 ECTS)

Part-time study

Since the winter semester 2022/23 it is possible to study the M.Sc. Data Science in part-time. The standard period of study is extended from 4 to 8 semesters compared to a regular full-time study. The study programme structure and in particular all modules are the same in both study types.
If you are planning to study Data Science in part-time and you are an international student, please inform yourself if your visa does extend accordingly. Also note that by changing your study course to part-time your amount of already studied semesters will double, e.g., 2 semesters in full-time equal 4 semesters in part time.
If you are planning to study Data Science in part-time and work simultaneously, please note that you are not allowed to work more than 20 hours/week to keep your state as student.

If you are already enroled for the full-time M.Sc. Data Science programme you can download an application form for changing your degree programme from the website of the Students Records Office. Please indicate clearly that you would like to change from a full-time study in M.Sc. Data Science to a part-time study.
Please note that deadlines concerning a late submission of documents, e.g., your Bachelor’s degree certificate, are not extended by changing into a part-time study.

Some additional information on the M.Sc. Data Science study programme in part-time can be found here.

Requirements

Admission requirements:

• A completed B.Sc. degree in Mathematics, Industrial Mathematics, Mathematical Economy, Computer Science, Data Science, or Physics from FAU or another equivalent domestic or international degree that is not significantly different with regard to the competence profile taught in the respective degree. Please note that your competence profile cannot be evaluated in advance, but only by the admission committee after completing the application process (described below).
• A Grade Point Average (GPA) of 2.5 or better with respect to the German grading system. Candidates with an admissible degree (described above) and a GPA between 2.6 and 2.8 are invited for a short online interview in which their knowledge in calculus, linear algebra, algorithms and data structures is evaluated. An online tool for converting grades to the German grading system using the Bavarian formula can be found here.
• English proficiency at level B2 CEFR (vantage or upper intermediate, not be older than 2 years) 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. A list of accepted language proficiency certificates can be found here.

Application:

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

• Registration for the winter intake is possible between 15th April and 31st May and for the summer intake between 15th October and 30th November. You can find the exact dates for the current intake periods on https://www.fau.eu/education/application-and-enrolment/applying-for-masters-degree-programmes/#bewerbungszeitraum
• Register online using the page www.campo.fau.eu
  Note: Applicants who have not yet an „ IdM Account “ have to register at IdM first (on www.campo.fau.eu ). 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: zuv-masterbuero@fau.de
  For further questions on the online application portal please send an email to: campo@fau.de

Furthermore read: Guide to the application process.

Services

Accommodation:

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.

Enrolment

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 „Campo„, which will help you to plan and manage your courses.

Important documents and further info

• Frequently Asked Questions (FAQ) v0.3
  A PDF document with answers to frequently asked questions about the M.Sc. Data Science study course programme 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.
  You can generate your study plan on the website of the Study Plan Generator (SPLAG). Make sure JavaScript is enabled in your browser.
• 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 daniel.tenbrinck@fau.de and you will be added to a dedicated mailing list in which occasional job offers are posted.