Data Science Master

The international English four semester Data Science Master with the degree Master of Science (M.Sc.) has started October 2017/2018 at Beuth University of Technology (App. Sc.) and will run annualy each winter-semester! The national and international application pages should open from 15th April to 15th of June for the next 22 seats. Most of the student questions are answered in the FAQ!

The initials of the selected students will be shown here in a few hours (firstname, lastname).

No Om, Al Se, Ma Ak, Om Ka, Sa Wa, Ur Vi, Po Ko, Ma Me, Ma Ho, Kr Na, Mo Ha, If Is, Ra Wa, Ri Mi, Ki Sc, Sh Bh, Za Az, Ro Wi, Sa Hi, Pa Kr, Pa We, Mu Ha, Fe Kh, Ch Te, Mi Ch, Ma Pa, Ka Pa, Sr Dh, Po Po, Vi Si, Ma Mo. Za Ab, Ta As, Ho Al, Li Mo, Pr Ku, Bi An.

Without any warranty! Errors happen!
Congratulations to all. And good luck in the future for those not accepted.

Goal of the Master

The master will qualify students to analyse big data efficiently and to create systems/solutions for Machine Learning. Therefore they will be all set for future industrial demands. The focus areas of this Data Science Master are "Urban Technologies" and "Intelligent Machines" and is explicitly interdisciplinary constructed.

After the basic knowledge of Data Science (as Computer Science and Statistics) we will teach a wide range of Machine Learning methods. Furthermore the most important tools, practices - as data preparation and big data analytics - will be taught and implemented practically. Additionally, we do have the possibility to work on an innovative idea right from the beginning to the end to create a start-up or together with leading companies in Berlin.

Furthermore, questions of ethics, responsibility and data protection as well as economic knowledge and analysis are integrated in order to convey an important change of perspective. The thesis on the Master of Science will be embedded in current research projects and industrial cooperation.

Please follow all related news for the Lab and the Master on our Twitter Account DSBeuth


Supported by

  • Berliner Qualitäts- und Innovationsoffensive (QIO 2016 – 2020) Förderlinie III b III. b) Hochschulübergreifende Maßnahmen für Innovation
  • Einstein Centre / Digital Future Berlin
  • BMBF Berlin Big Data Center BBDC
  • BMWi Smart Service Welt  - MACCS
  • BMWi Smart Data -  Smart Data Web und ExCELL
  • EU H2020 - FashionBrain