Foto: Anna Logue

Mannheim Master in Data Science

    Acknowledgement of Examinations

  • What conditions must be met for an acknowledgement of examinations?

    • The acquired skills of the module that you want to have acknowledged have to overlap significantly with the module to be replaced in Mannheim.
    • Before you apply, please check the equivalence of the course content, learning outcomes and qualification goals by comparing the Mannheim module descriptions with the content and scope of your courses.

    Note: The conversion ratio of semester hours per week (SWS) on ECTS is about 1.5. A module with a workload of 6 ECTS credits corresponds to approximately 4 hours per week for example. Also, several courses / modules can be combined to cover a corresponding equivalent content.

  • How does the acknowledgement of examinations work?

    Applications to the examination board must be submitted in writing. Please remember to provide your address, matriculation number and e-mail address if any queries become necessary. Please also state the semester in which you started your Master's degree program, so that we can assign your request directly to the valid examination regulations.

    Studies and examinations that you have provided in University studies at a state or state-acknowledged university (home and abroad) or cooperative education may be credited upon request, provided the proven competencies match substantially with those of the MMDS degree program in Mannheim.

  • What documents are needed for recognition?

    For the acknowledgement you have to hand in the following documents:

    • A written, informal application for recognition of examinations including information about which completed modules you would like to recognize and which modules of the University of Mannheim will be replaced by these.
    • Original certificate or a current, officially certified transcript (Transcript of Records). When not issued in German or English certificates please submit an officially certified German or English translation.
    • Documents from which the acquired skills emerge (in brief) and the content of the course(s) to be recognized. Without proof of the competencies acquired in the module/course your request can not be processed! This can for example be excerpts from the description of modules with learning goals and learning objectives or learning outcomes from course catalogs, study guide, notes or scripts with references, or - if necessary - printouts from the Internet or links to relevant information on the Internet.
    • Send us this information in electronic form and as printout.
  • Where can I submit the documents for an acknowledgement of examinations?

    Please hand in the complete documents in notarized hardcopy to the examination board.

    Note: A final decision on possible debits can be made only after enrolling at the University of Mannheim. Unfortunately, preliminary information cannot be provided.

  • Studyplan and Timetable

    The Mannheim Master in Data Science (MMDS) program equips students with the knowledge and skills necessary to gain operational insight from large and complex datasets. It is structured into the five blocks Fundamentals, Data Management, Data Analytics, Projects and Seminars, and the Master’s Thesis.

    Fundamentals (0 – 14 ECTS)

    The goal of the fundamentals block is to align the previous knowledge of students from different degree programs. Graduates from computer science and mathematics acquire the required knowledge in empirical research (in particular, data collection and multivariate statistics). Graduates from the social sciences and other fields acquire the required knowledge 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 block covers methods and concepts for obtaining, storing, integrating, managing, querying, and processing large amounts of data. The block 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 block 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 block introduces students to independent research and teaches the skills necessary to successfully participate in and contribute to larger data science projects. The block consists of research seminars, individual projects, team projects, as well as data science competitions. The projects are conducted jointly with industrial partners and/or support ongoing research efforts of participating institutes.

    Master’s Thesis (30 ECTS)

    In the master thesis, students apply what they learned throughout the program. The master thesis has a duration of 6 months. Students are encouraged to write their thesis either in the context of research projects conducted by participating institutes or together with an industrial partner. Students often write their master thesis together with a company from the MMDS Industry Partner Network.

    Note: This is just an example - your actual study plan may vary depending on the semester in which you start, your preferences, etc.

  • Examination Regulations and module catalogue

    The module catalogue 2019/2020 (Appendix) gives an overview of the courses and contents of the program.

    Please read the examination regulations of your program carefully.

  • Examination board

    Members of the examination board for the course Mannheim Master in Data Science:


    Prof. Dr. Paulheim

    Student representative

    Alexander Haberling

    You can contact the examination board via pruefungsausschuss(at)           

    The official address is:

    Prof. Dr. Heiko Paulheim

    Prof. Dr. Heiko Paulheim

    Prüfungs­ausschuss Mannheim Master in Data Science
    Universität Mannheim
    Fakultät für Wirtschafts­informatik und Wirtschafts­mathematik
    B 6, 26 – Raum B 0.22
    68159 Mannheim
    Di 9:00–10:00
    vorherige Termin­vereinbarung mit Bianca Lermer
  • Advisory Service

    The student advisory (run by the study coach and the academic advisor) is a combined offer for the Master Degree of Mannheim Master in Data Science.  

     You can consult us in questions regarding:

    • the study program
    • the module catalogue
    • the examination regulations
    • cases of hardship
    • questions about module changes
    • contents of courses
    • your study plan (modules, tracks, thesis)

    You can contact the advisory service via studienberatung(at)

    Samuel Broscheit

    Samuel Broscheit

    Fach­studien­beratung Wirtschafts­informatik und Lehr­amt Informatik
    Universität Mannheim
    Fakultät für Wirtschafts­informatik und Wirtschafts­mathematik
    B 6, 26
    Gebäudeteil B – Raum B 0.10
    68159 Mannheim
    nach Vereinbarung
    Birgit Czanderle, M.A.

    Birgit Czanderle, M.A.

    Studien­erfolgskoordinatorin, Studien­coaching Wirtschafts­informatik und MMDS, Kontaktstelle zum Prüfungs­ausschuss
    Universität Mannheim
    Fakultät für Wirtschafts­informatik und Wirtschafts­mathematik
    B 6, 26
    Gebäudeteil B – Raum B 0.05
    68159 Mannheim
    nach Vereinbarung
    Mo–Mi 10–13.30 Uhr
    Lisa Wessa, M.A.

    Lisa Wessa, M.A.

    in Elternzeit
    Kontakt zur Fakultät WIM

    Kontakt zur Fakultät WIM

    Universität Mannheim
    Fakultät für Wirtschafts­informatik und Wirtschafts­mathematik
    B 6,26
    68159 Mannheim
    bis Fr., 13.12.2019 telefonisch: Di, 12.30–13.30 Uhr und Fr, 10–11 Uhr
  • Master’s Thesis

    In order to find a supervisor and a topic for the master thesis, early planning and initiative is necessary. First and foremost, as a student, you are responsible for finding direct contact with a chair that fits your study profile. This means that you proactively contact several chairs / professors to get a topic for your Master's thesis. It is most common that you find an agreement in this way and this way is desired by the faculty of you as a student. 

    Who can supervise my Master's thesis?

    In general, all professors teaching in the MMDS also supervise master theses. However, professors from other faculties are also possible, given that the topic falls in the scope of the MMDS.

    Industry Partner Network

    MMDS students often write their master thesis together with a company from the MMDS Industry Partner Network. For more informaiton about open topics provided by partner companies please contact Prof. Dr. Heiko Paulheim.