Data Science Certificate Program

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Data Science Certificate Program

In the decade of "big data" where data is recorded in almost all areas of daily life, ever larger and more complex data is available. The skill of analyzing and extracting information from such data has become an indispensable core competence in research and industry.

As the demand for suitably trained data scientists having these skills is already noticeable and will probably continue to grow steadily, the Faculty of Mathematics, Informatics and Statistics of the LMU decided to develop a Professional Certificate Program in collaboration with the Unit for Professional Training at the LMU. The certificate program is intended to create a further training path for employees who already have a university degree and a job in a similar field.

You can also download our  Flyer.


Program: Professional Certificate Program

Duration of the Program: 10 days in total, always on Fridays from 9 a.m. - 5 p.m. (in the winter term between October and February, and in the summer term between April + July), see also further information here.

Language of instruction: English (unless all participants understand German well enough, however, the course material itself will always be in English)

Application deadline: 29th of February 2020 for the summer term (Apply here)

Participation Fee:

  • 6500 € regular fee (includes 5900 € course fee and 600 € examination fee).
  • 5200 € (includes 4720 € course fee and 480 € examination fee) reduced fee for applicants from social, scientific and cultural facilities.
  • If a company is a sponsoring member of the German Society for Data Science (GDS) e.V. and registers more than four applicants who are admitted to the course by the Steering Committee, a discount of 20% will be granted per participant.
  • Please send a proof with your application. 

Workload: about 200 hours (including time for preparation for the lectures, follow-up work and preparation for the exam).

Highlights of the Program:

  • Interdisciplinary program between statistics and computer science.
  • Good balance of methodological training and use case analysis.
  • Wide range of taught topics (see curriculum for more information).
  • Introduction to theoretical and practical concepts in the field of data science.
  • For more information follow this link.