Please note: The conversion ratio of credit hours to ECTS credits is about 1.5. This means for instance that a module with a workload of 6 ECTS credits corresponds to approximately 4 credit hours. It is also possible to combine several courses or modules to cover equivalent contents.
Requests must be submitted to the examination committee in writing. Please make sure to indicate your address, student ID number and e-mail address in case we need to contact you for follow-up questions. We also ask you to state the semester in which you started your master's program so that we know straight away which examination regulations apply to you.
Coursework and examinations that you completed in the course of a degree program at an official or officially recognized higher education institution (in Germany or abroad) or at a public university of cooperative education (Berufsakademie) may be credited upon request, provided the competences acquired largely correspond to those taught in the MMDS program in Mannheim.
In order to have your coursework and examinations recognized, you need to hand in the following documents:
Please hand in the complete documents, including the certifications listed above, to the examination committee.
Please note that we cannot make a final decision on possible recognition until after you have enrolled at the University of Mannheim. Unfortunately, we are therefore unable to give you any definite information on this matter beforehand.
The Mannheim Master in Data Science (MMDS) program equips students with the knowledge and skills necessary to gain operational insight into large and complex datasets. It covers five major areas: Fundamentals, Data Management, Data Analytics, Projects and Seminars, and the Master’s Thesis.
Fundamentals (0 – 14 ECTS)
The goal of the Fundamentals area is to align the different knowledge students acquired in their previous degree programs. Graduates of computer science and mathematics programs will acquire the knowledge required in empirical research (in particular, data collection and multivariate statistics). Graduates of social sciences programs and other fields will acquire the knowledge required in computer science (in particular, programming and database technology).
Data Management (24 – 36 ECTS)
One of the central challenges in the Big Data area is to handle the enormous amount, speed, heterogeneity, and quality of the data collected in industry, the public sector, and science. The Data Management area covers methods and concepts for obtaining, storing, integrating, managing, querying, and processing large amounts of data. The area includes courses on modern data management technology (such as parallel database systems, Spark, and NoSQL databases), data integration, information retrieval and search, software engineering, and algorithms.
Data Analytics (30 – 54 ECTS)
The Data Analytics area forms the core of the study program. It provides courses ranging from data mining, machine learning, and decision support, over text analytics and natural language processing, to advanced social science methods such as cross-sectional and longitudinal data analysis. The range of methodological courses is enhanced by courses on optimization, visualization, mathematics and information, and algebraic statistics.
Projects and Seminars (12 – 16 ECTS)
The Projects and Seminars area introduces students to independent research and teaches the skills necessary to successfully participate in and contribute to larger data science projects. The area consists of research seminars, individual projects, team projects, as well as data science competitions. The projects are conducted jointly with industrial partners and/
Master’s Thesis (30 ECTS)
In the master’s thesis, students apply what they learned throughout the program. The master’s thesis is written over a period of six months. Students are encouraged to write their thesis either in the context of research projects conducted by participating institutes or in cooperation with an industrial partner. Students often write their master’s thesis in cooperation with a company from the MMDS Industry Partner Network.
Please note: This is just a sample degree plan - your actual degree plan may differ depending on the semester in which you start, your preferences, etc.
The module catalog gives an overview of the course and contents of the program.
Please read the examination regulations of your program carefully.
You can contact the examination committee via pruefungsausschuss(at)wim.uni-mannheim.de
Tasks of the central examination committee
We are responsible for the following topics:
For further information, please contact:
The student advisory (run by the study coach and the academic advisor) is a combined offer for the Mannheim Master in Data Science program.
You can consult us in questions regarding:
You can contact the advisory service via studienberatung(at)wim.uni-mannheim.de.
Finding a supervisor and a topic for your master’s thesis requires early planning and initiative on your part. It is primarily your responsibility as a student to contact a chair that fits your study profile. This means that you need to contact several chairs or professors on your own initiative in order to find someone who will agree to supervise your master's thesis. It is the norm that students contact the chairs personally, and something we expect from students at our school.
Who can supervise my master's thesis?
In general, all professors teaching courses in the MMDS program also supervise master’s theses. However, professors from other schools and departments may also do so, provided the topic falls within the scope of the MMDS program.
Industry Partner Network
MMDS students often write their master’s thesis in cooperation with a company from the MMDS Industry Partner Network. For more information about topics offered by partner companies, please contact Prof. Heiko Paulheim.