Foundations in Data Science
	Mathematical Foundations	ECTS	Computer Science Foundations	ECTS	Language Foundations
1. sem.	Advanced Mathematics 1	10	Digital Engineering 1	5	German or English up to niveau C.1 30 ECTS (5 ECTS per semester)
	Math. Training I	5	Introduction to Computational Machine Learning	5
2. sem.	Advanced Mathematics 2	10	Digital Engineering 2	5
	Math. Training II	5	Math. Programming	5
3. sem.	Advanced Mathematics 3	10
	Math. Modelling	10
	Applied Optimization	5
4. sem.	Advanced Mathematics 4	10	Statistical Modeling	5
	Numerical Methods	10
	Specialization			ECTS
5. sem.	Proseminar			5
	Compulsory Elective Modules			20
6. sem.	Compulsory Elective Modules			10
	Bachelor Thesis			15

Admission requirements:

Admission to the Bachelor's degree programme requires a successfully completed Abitur (A-levels) and English language skills at level B2 (GER).

Required German language skills for international prospective students:

No knowledge of German is required for admission. However, as German courses are an integral part of the programme, students without proven German language skills must take corresponding language modules. Depending on the entry level, these lead to at least language level B1/B1+ (GER).

Prerequisites:

Applicants should be familiar with the General University Entrance Qualification subject matter in Germany. Every applicant should therefore be able to pass the test in this link.
https://bildungsportal.sachsen.de/opal/auth/RepositoryEntry/8215396360

Application:

To apply, you should definitely take a subject-specific entrance test. You can start this test here.
Please save the results of this test as a pdf file. You should then upload this file in the application process.

• If you have a German ‘Abitur’ then you can directly enrol via the ‘Bewerbungsportal’. Please follow the instructions at
  https://www.tu-chemnitz.de/studierendenservice/studserv/bewerbung/index.php#deutsche_studieninteressenten
  and mind the application deadlines.
• If you are coming from abroad then you need to apply via uni-assist. Please follow the instructions at
  https://www.tu-chemnitz.de/studierendenservice/studserv/bewerbung/index.php.en#ausl_studieninteressenten
  and mind the application deadlines.

  You can find a list of the necessary application documents on the page General Information on "How to apply at University of Technology Chemnitz".

  Upload the application documents and the results of the subject-specific entrance test at uni-assist.
  (One way to find the program "Foundations in Data Science" in the uni-assist data base is as following. Once you have registered, please log in and go to the "Search courses of study" section. In the drop down menu "University" and "Degree type", please select "Technische Universität Chemnitz (TU)" and "Bachelor", respectively, and click "Search". Please note that the resulting list is not shown alphabetically and that the program "Foundations in Data Science" may be shown on later pages of the list of shown programs.)
  

 

Deadlines for applications are: 15th of July (applications for fall term, starting October 1st), 15th of January (applications for spring term, starting April 1st)
Apply as early as possible, at least 8 weeks before the deadline.

 

Some general information for international students at TU Chemnitz.

 

Please note that applications with incomplete application documents will not be evaluated!

 

Visa for international students: As an international student, you need a visa and proof of sufficient funds. The visa process can take several months, so we recommend applying for a visa in good time. You can usually make an appointment to apply for a visa before you receive your admission letter. But you will definitely need your admission letter at the appointment. Learn about the specific documents required for your visa application, the required level of financial proof, and the available funding options for your studies in Germany. More information about Student Visa Germany and Blocked Account

Data scientists are characterised not only by their sound theoretical and practical knowledge, but also by their ability to think logically and abstractly, proceed analytically, communicate precisely, show perseverance in solving complex problems and work effectively in a team. These skills are at the heart of the Foundations in Data Science degree programme in Chemnitz, which also stands out due to its particularly high proportion of mathematics. It is precisely these skills, combined with in-depth mathematical training and state-of-the-art data analysis methods, that open up excellent career prospects for graduates. Another core feature of the degree programme is the close link between mathematics and computer science. This combination enables students to master data-based challenges in practice and to analytically solve complex problems from various fields of application. In the age of digitalisation, in which data increasingly forms the basis for decisions and innovations, interdisciplinary collaboration between mathematics, computer science and applied sciences is becoming more and more important. The Data Science degree programme in Chemnitz therefore offers numerous options in technical, natural and social science minor subjects to promote these links.

Graduates of the Bachelor's degree programme in Data Science are versatile and can find exciting career opportunities for example in the following industries and fields of activity:

• Data analytics and business intelligence
• Management and IT consulting
• Banks and financial services
• Technology centres and high-tech companies
• Software development and artificial intelligence
• Telecommunications and network analysis
• Medical data analysis and bioinformatics
• Logistics and supply chain optimisation
• Automotive industry and smart mobility

Graduates of the advanced Master's programme in Data Science also have the best prerequisites for management positions in business or for an academic career in research and teaching. Thanks to the high demand for data science skills, the unemployment rate in this field is extremely low. According to gehaltsreporter.de, starting salaries Saveraged 55,790 euros gross.

About the study programme

Prof. Dr. Martin Stoll

Karriere:

Dipl. Math. Chemnitz, PhD Oxford, University of Oxford, MPI Magdeburg

Forschung:

Numerical Linear Algebra, Numerical Analysis, Data Science

Lehre:

Matrix Methoden in Data Science, Numerische Lineare Algebra, Numerik

E-Mail:

martin.stoll@math.tu-chemnitz.de

Prof. Alois Pichler PhD

Karriere:

Studium Math. und Physik Universität Wien, PhD Universität Wien, tätig in der Versicherungindustrie

Forschung:

Statistics and Probability Theory, Optimization under Uncertainty, Finance, Risk Theory

Lehre:

Statistische Methoden in Data Science, Stochastische Optimierung

E-Mail:

alois.pichler@math.tu-chemnitz.de

Prof. Dr. Oliver Ernst

Karriere:

Dipl. Math. Karlsruhe, PhD Stanford, University of Maryland, TU Freiberg

Forschung:

Uncertainty quantification, Numerical Linear Algebra, Numerical Analysis

Lehre:

Einführung Data Science, Uncertainty quantification, Numerische Lineare Algebra

E-Mail:

oliver.ernst@math.tu-chemnitz.de

Prof. Dr. Uta Freiberg

Karriere:

Dipl. Math. HU Berlin, PhD FSU Jena, La Sapienza Rom, ANU Canberra, Uni Stuttgart

Forschung:

Stochastic processes, Fractals, Energy forms

Lehre:

Stochastik, Stochastische Prozesse, Fraktale

E-Mail:

uta.freiberg@math.tu-chemnitz.de

Prof. Dr. Christoph Helmberg

Karriere:

Dipl. Ing. TU Graz, PhD TU Graz, Zuse Institut Berlin

Forschung:

Discrete Optimization, Semidefinite Programming, Convex Optimization

Lehre:

Diskrete Optimierung, Graphentheorie, Nichtlineare Optimierung

E-Mail:

christoph.helmberg@math.tu-chemnitz.de

Prof. Dr. Daniel Potts

Karriere:

Dipl. Math. Rostock, PhD Lübeck,

Forschung:

Fourier Analysis, NFFT, Fast summation methods, Big Data Learning

Lehre:

Sparse and High-Dimensional Approximation, Einführung in die Fourier-Analysis

E-Mail:

daniel.potts@math.tu-chemnitz.de

Prof. Dr. Vladimir Shikhman

Karriere:

Dipl. Math RWTH Aachen, PhD RWTH Aachen, Catholic University of Louvain

Forschung:

Economic Equilibrium Analysis, Nonsmooth Optimization

Lehre:

Big Data Analytics, Mathematik im Investmentbanking

E-Mail:

vladimir.shikhman@math.tu-chemnitz.de

Study documents (Draft version)
Study regulations [GER] Examination regulations [GER] Study schedule [GER] Module directory [GER] Poster

Study advisor