Data Science (M.Sc.)

Degree:Master of Science (M.Sc.)
Duration:4 semesters
Start:Winter semester
Teaching language:Englisch
ECTS credits:120


  • Language Level B2 in English
  • At least 20 ECTS in Mathematics & Statistics, e.G. Analysis, Linear Algebra, Numerical Analysis, Probability Theory, Data Modelling
  • At least 25 ECTS in Computer Science, e.g. Programming, Databases, Distributed Systems, Application Programming
  • A degree in "Medieninformatik", "Technische Informatik – Embedded Systems" or "Mathematik" of Beuth-Hochschule für Technik Berlin or an equivalent Bachelor with at least 180 ECTS will fulfil the ECTS criteria according to ZO §3 (2) a

Application Period:

  • For the winter semester: 02th May – 15th June

Guide to the application process in english


Our curriculum offers a mix of advanced computer science methods and mathematics. As a Data Science Student, you will learn to master scalable database technologies and design statistical knowledge to build prediction models.

Data Science enables companies to design new data products and learning how to sell and promote these products is an essential requirement. You will learn to measure data products and communicate their usefulness in a commercial settings.

After covering the Data Science basics (in Computer Science and Statistics), you will learn a wide range of Machine Learning methods. Furthermore, many of the important tools and practices - such as data preparation and big data analytics – will be taught and implemented in practice.

Additionally, you will have the possibility to work on an innovative idea from beginning to end to create a start-up or to collaborate with leading companies in Berlin. Important considerations such as ethics, responsibility, data privacy and economics of data products are also incorporated. Your Master‘s thesis will contribute to a current research project, industrial cooperation or a startup idea.

The Master‘s degree program in data science pays special attention to practical relevance and continuously updates course content to constantly changing situations in data science. This is reflected in design and project development themes, where students will work each semester on an industrial-, research- or their own start-up project.

Our experienced instructors will teach you a broad range of problem-solving methods. Typical aspects of your future professional practice – individual work, teamwork and cooperative management – are an integral part of the curriculum.

The "Data Science" research group started in 2015. Six professors and several PhD students work on both fundamental and applied research problems. We investigate systems capable of marrying up text, tables, photos or even GPS coordinates and, most importantly, making sense of them.

Our major research activities are: NLP/Machine Understanding, Deep Learning on GPUs and CPUs, Massive Parallel Processing (MPP) Engines, Spatial Data Bases and Learning Analytics. Research projects are funded by EU H2020, BMBF and BMWi and are in partnership with international academic and top-tier industry partners.

Qualified students will earn a Master of Science (M.Sc) degree after four semesters.

Successful use of data science at a large organization is challenging, but once data science methods are implemented, an organization sees everything through the lens of its potential. Organizations located in Berlin like Zalando, Amazon, eBay/ or SAP and many medium-sized companies, rely heavily on data science and utilize its capabilities in all their products. As a Data Scientist or Data Engineer you design models for data analysis, collect training data, clean up the data and run models on many computers simultaneously.

Berlin’s business culture is result-oriented. It’s not so much about who you are but about what you bring to the table and how hard you work. Everyone in the field open and keen to pursue their ideas. Working in in data science gives you access to incredibly smart and driven people from all over the world that come to Berlin to work in the thriving Internet sector.

Beuth-University also is part of Berlin’s vibrant startup scene. The combination of affordability with a high standard of living, a vibrant cultural scene and an international outlook foster an exciting atmosphere of new ideas and startups.

  • Amazon AWS (Berlin)
  • Axel Springer Verlag, Welt/N24 (Berlin)
  • Bayer SE (Berlin)
  • Bundesdruckerei (Berlin)
  • Charité Universitätsmedizin (Berlin)
  • Deutsches Forschungszentrum für Künstliche Intelligenz (DFKI) GmbH (Büro Berlin)
  • eBay Inc. / (Berlin/Kleinmachnow)
  • ENTIRETEC AG (Dresden)
  • Feld M GmbH (München)
  • Fraunhofer FOKUS Berlin
  • GmbH (Berlin)
  • Helios Kliniken GmbH (Berlin)
  • HERE GmbH (Berlin)
  • Holtzbrink Konzern, Die-Zeit-Online (Hamburg/Berlin)
  • IBM Deutschland GmbH (Köln)
  • Inovex GmbH (Pforzheim, Karlsruhe)
  • Neofonie GmbH (Berlin)
  • SAP SE, Innovation Center Potsdam
  • Siemens AG (München)
  • SmartPatient GmbH (München)
  • SpringerNature Verlag (Berlin)
  • ubermetrics GmbH (Berlin)
  • Vico Research & Consulting GmbH (Stuttgart)
  • webTrekk GmbH (Berlin)
  • Zalando SE (Berlin)


1. Term
Module Module name SU SWS Ü SWS LP P/WP FB
M01 Mathematical Models 4   5 P II
M02 Advanced Software Engineering 2 1 5 P VI
M03 Statistical Computing 2 1 5 P II
M04 Practical Data Science Programming 2 2 5 P VI
M05 Computer Science for Big Data 2 1 5 P VI
M06 Business Intelligence and Data Science Platforms 4   5 P VI
2. Term
Module Module name SU SWS Ü SWS LP P/WP FB
M07 Data Visualization 2 2 6 P II
M08 Regression 2 2 6 P II
M09 Machine Learning I 2 2 6 P II
M10 Applications 1: Data Science Workflow/Applications 4   7 P VI, II
M11 Required-Elective Module 1   4 5 WP VI
  Required-Elective Modules          
WP01 Text Mining and Natural Language Processing   4 5 WP VI
WP02 Deep Learning   4 5 WP VI
3. Term
Module Module name SU SWS Ü SWS LP P/WP FB
M12 Machine Learning 2 2 2 5 P II
M13 Applications 2: Urban Technologies 4   5 P VI
M14 Applications 3: Enterprise Data Science 4   5 P VI
M15 General Studies 1 2   2,5 WP I
M16 General Studies 2   2 2,5 WP I
M17 Business Value and Responsibility 4   5 P I
M18 Required-Elective Module 2   4 5 WP II oder VI
  Required-Elective Modules          
WP03 Advances in Machine Learning   4 5 WP VI
WP04 Learning from Images   4 5 WP VI
WP05 Sampling and Design   4 5 WP II
WP06 Learning Optimization   4 5 WP II
4. Term
Module Module name SU SWS Ü SWS LP P/WP FB
M19 Final Examination Module     30 P  
M19.1 Master’s Thesis     25 P  
M19.2 Oral Final Examination     5 P  

Source: Amtliche Mitteilung, 42. Jahrgang, Nr. 05/2021 vom 24.11.2020

SWS: Hours per week (Semesterwochenstunden), SU: Seminar (Seminaristischer Unterricht), Ü: Practice (Übung), P: Required Module (Pflichtmodul), WP: Required-Elective Module (Wahlpflichtmodul), LP: Credits (Leistungspunkte), FB: Department (zuständiger Fachbereich)