Mathematics for Data and Resource Sciences


Faculty Fakultät 1 - Mathematik und Informatik
Degree Master of Science (M.Sc.)
Standard period of study 4 Semester
Start of studies Wintersemester
Admission requirement

B.Sc. in Mathematics or equivalent, TOEFL or IELTS, 1 DIN A4 letter of motivation, qualification interview

Application deadline 15 April
Language englisch

Study concept

Mathematical skills are indispensable for the activities of modern industrial enterprises. In addition, the necessary quantification, professional assessment and documentation of the findings of application-oriented research can only be carried out with the advanced and qualified use of mathematical methods. The current issue of climate change and the challenges associated with it make the management of large data volumes an essential skill, while an understanding of the technical difficulties involved in the provision, transportation and recycling of valuable waste materials and by-products has become invaluable for companies today. Based on a top-class mathematical foundation – ideally laid upon the groundwork of a bachelor's degree in mathematics – the Mathematics for Data and Resource Sciences programme focuses on the methods and techniques necessary to understand and mathematically address the challenges described above.

Programme Objective

Successful graduates of the Master's programme Mathematics for Data and Resource Sciences will have acquired the techniques, methods and general mathematical skills to solve the most pressing problems of today. These include the ability to understand and exploit large amounts of data, a mastery of so-called computer-based machine learning as well as a broad understanding of problems in the field of scarce resources – such as rare raw materials, in particular, or planet Earth in general.

Application-Oriented Lecture Series

Mathematical problems from real-world applications are discussed in a lecture series created specifically for the Master’s programme. This series provides a catalogue of relevant problem statements at an early stage, so that students can apply the skills they have learned in a meaningful way. The clusters of complex issues presented go straight to the heart of the individual research areas, and are constantly updated in order to gain the deepest insights into mathematical problems.

Industrial Internship

The Master’s programme rewards student commitment to companies based in the region. If an industrial internship of at least four months’ duration is completed during the degree programme, this time can be recognized in credits that count towards the completion of the Master's thesis: The maximum time required for completing the thesis is reduced from 9 months to 6, and the content required is also reduced accordingly. This measure is intended to benefit the local economy in the form of expedited graduation and the precise skills acquired by the students. Meanwhile, the students themselves gain by taking their first steps towards permanent employment in the region.

Master's Thesis Parallel to Lecture Programme

The range of courses offered in this Master's degree programme is broad, guaranteeing a truly comprehensive and multi-faceted education. In order to expand this breadth of opportunity to all four semesters, the programme has much greater flexibility built into it than other courses, with students allowed to begin working on their Master's thesis as early as the third semester.

Certification for Specialized Training

Within the framework of the international Master's degree, it is possible to obtain extra certification if in-depth knowledge in Mathematical Data Science or Geomathematics is acquired during the degree programme. By means of their respective specialization – whereby the former focuses more on the processing and treatment of (large) data volumes and the latter more on the mathematical problems associated with climate change or the circular economy – graduates are eminently prepared for the real-world requirements of professional practice.



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Job opportunities

IT Companies

Google, IBM

Financial Sector/Insurance

Data analyst

Environment-related industries

Optimization of software in wastewater treatment plants, resource optimization in industrial production processes, materials sciences, etc.

Corporate consultancy

Data analyst

Automotive Industry

Autonomous driving, software security, etc.


Please do not apply unless you already satisfy the admission requirements at the time of application. If you do not meet these requirements, your application will not be considered in the selection process.

Please apply via the online application portal. After a self-registration you will receive your personal login data and you can proceed. We look forward to receiving your application!

Application period

  • 01.01.-15.04. for winter semester

Admission Requirements

  • Bachelor's degree in mathematics or equivalent (graduate status)
  • English language skills: TOEFL with 90 points (internet-based test) / IELTS with a score of 6.5
  • Letter of motivation
  • Qualification interview
  • Successfully completed modules from the catalogue of requirements

Catalog of requirements


Functions of more than one variables
Metric Spaces
Implicit Function Theorem
Criteria for Existence and Uniqueness of Ordinary Differential Equations

Banach spaces
Hilbert Spaces
Hahn—Banach Theorem
Open Mapping Theorem 

Discrete Mathematics/Algebra

set-theoretic and logical foundations   
order theory   
algebraic structures and homomorphisms   
linear algebra (vector spaces, linear operators, duality theory, eigenvalues) 

basics of graph theory and combinatorics


Newton's method 
direct and iterative methods for linear systems of equations
least squares problems
interpolation using polynomials
numerical integration using Newton-Cotes 
fluency in one programming language (e.g. Python, Matlab, C/C++, Fortran)

Newton's method in higher dimensions
Gauss integration
Matlab or Python programming


Simplex algorithm
duality in linear programming
separation theorems
KKT conditions
constraint qualifications
sufficient optimality conditions
Lagrange duality

optimality conditions for convex optimisation problems
Newton's algorithm
gradient descent methods
penalty methods
optimisation on graphs


Probability measure and sigma algebra
Distribution functions and moments
Random variables
Conditional probability
Law of large numbers and central limit theorem
Estimators and confidence intervals
statistical hypothesis testing
Linear Regression

Measure theory
Conditional expectation
Stochastic processes