Fachbereich Wirtschaftswissenschaft

M.Sc. Data Science in Business and Economics

Why Data Science?

Data Science continues to be a vibrant, interdisciplinary and rapidly growing field. More and more firms and institutions are hiring data scientists, and data scientist has been one of America's top jobs for many years in a row according to Glassdoor.

Data scientists must be able to acquire, understand, and analyze complex and unstructured data, to interpret the meaning of the results, and to communicate the results to relevant stakeholders. Hence, Data Science requires a unique combination of (1) econometrics and Machine Learning, (2) programming skills, and (3) domain knowledge.

The surging interest in AI technologies, such as Large Language Models, highlights the need for firms and managers to possess a good understanding of their capabilities and limitations. This emphasizes the importance of data science education in empowering professionals with the critical thinking and technical skills required to use these tools responsibly, customize solutions them to solve specific problems, and to make sound decisions in a data-centric world.

Why this program?

This four-semester M.Sc. program reflects these requirements to provide its graduates with a unique skill set including state-of-the-art development in Machine Learning. Students who enroll in this program specialize in econometrics and select further modules from a broad set of options on different aspects of business and economics. On top of that, students complete modules on data science techniques, which includes programming in R and Python as well as the possibility to enroll in courses in Machine Learning in collaboration with the computer science department.

A unique element of the program is the Data Science Project, which is typically part of the 3. semester. Students form teams and design and execute a data science project including data collection, data cleaning, modeling and analysis, and presentation. The final product is usually an App or a dash board that allows the user to explore the problem and the solutions. Example projects include the following:

  • Students trained a model to evaluate the safety of streets at night. The outcomes of this model served as the foundation for a navigation app that helps users choose the safest routes during nighttime.

  • Students developed a model to detect exits on highways (Autobahn) and analyze the adjacent surface structure to pinpoint potential locations for solar parks on underutilized land.

  • Students created a machine-learning pipeline to recognize surface structures in major cities. This was combined with satellite-measured surface temperature data to identify urban heat islands.

  • Students designed a bicycle navigation app that employs a specially developed machine learning pipeline to identify bike-friendly routes.

Why Tübingen?

The University of Tübingen, along with its surrounding ecosystem, stands out as a leading hub for Machine Learning and AI research in Europe. It is home to notable institutions such as the Machine Learning Cluster of Excellence, the Tübingen AI Center, the Max-Planck-Institute on Intelligent Systems, and Cyber Valley. This creates a great environment rich in advanced research, diverse study programs, and a wealth of institutions dedicated to Machine Learning. This unique combination is rare globally, providing our students with unparalleled opportunities. Our faculty actively contributes to and benefits from this thriving environment, e.g., with several professors being members of the Machine Learning Cluster of Excellence

If you're passionate about Machine Learning, AI, Data Science, and their applications in business and economics - come study with us!


Key Facts

4 Semester Program

Courses start in October

120 ECTS Credits

incl. Master Thesis (30 ECTS credits)

English and German

Course Language

Areas of Specialization

  • Econometrics
  • Business and Economics
  • Data Science Techniques, e.g.:
    • Algorithms*
    • Basics of Machine Learning*
    • Big Data Computing
    • Causal Inference
    • Causal Machine Learning
    • Data Science Project Management
    • Database Systems*
    • Deep Learning*
    • Machine Learning Applications in Business and Economics
    • Practical Deep Learning for Language Processing
    • Practical Deep Learning from Visual Data
    • Probabilistic Machine Learning*
    • Statistical Machine Learning*

* in cooperation with the Dept. of Computer Science


Modules / Areas ECTS credits  
Core Studies 27  
Specialization Studies 12-24



Elective Studies 0-18
Data Science Project 12  
Advanced Topics 9  
Master Thesis 30  


Ph.D. Track

Graduates will also find themselves well-positioned to pursue a Ph.D. subsequent to this M.Sc. program. For this purpose, students may study this M.Sc. program on a Ph.D. track, putting themselves on a fast track to a doctoral degree.

Module Handbook

For more detailed information about the modules, please see the Module Handbook.



Application for all Master Programs is possible from March 1st until May 15th
(M.Sc. Economics: Until July 15h for international / September 15th for EU applicants)

Application Portal

Bachelor Degree Requirements

 Bachelor Degree Certificate in Business Administration, Economics or related fields with a final grade of at least 2.5 or better according to the German grading system. (The final grades of international applicants will be converted into the German grading system and therefore also be subject to this rule).

  • A certificate stating that the applicant has successfully completed a first university degree or a certificate (or equivalent documents) providing evidence of the successful completion of at least the year prior to his/her final examination in the equivalent studies.
  • Degree should preferably amount to 180 ECTS.

Complete Transcript of Records with a final grade or calculated grade average.

The transcript should prove the applicant has solid intermediate knowledge in

  • Statistics / Mathematics / Econometrics
  • Business Administration
  • Economics

Further, applicants must demonstrate their experience in writing code in a statistical programming language (preferrably R or Python). Applicants can prove their experience

  • by showing that they have successfully passed modules in their qualifying degree that involve students' writing code (preferrably R or Python), in this case, applicants must provide detailed descriptions of the relevant courses, or
  • by providing documents that demonstrate their skills/experience in writing code in a statistical programming language (preferrably R or Python), e.g., part of a B.Sc.-thesis, or
  • by providing other documentation that establishes the applicant's skills/experience in writing code in a statistical programming language (preferrably R or Python), e.g. certificates of online learning platforms that offer programming for Data Science courses.
  • In all cases, it is important that applicants provide clear documentation of their skills that allows the selection committee to make an informed assessment.

Transcripts of Records containing generic module titles (such as Business Administration I, II and III) should be accompanied by detailed module descriptions.



CV in tabular format in English or German

Letter of Motivation

Please provide us with a letter of motivation explaining your reasons for applying for this degree and outlining your qualifications for it. Kindly limit yourself to one page, the letter may be composed either in English or in German.

Language Requirements

  Proof of English Language Qualification

  • TOEFL ibt: 79 or
  • TOEFL pbt: 550 or
  • IELTS: 6.5 (overall band) or
  • Cambridge: CAE, CPE or BEC Higher or
  • TOEIC: 700 or
  • UniCert III Certificate or
  • German Abitur including at least 6 (G8) or 7 (G9) years of English or
  • Officially recognized higher education degree (e.g. Bachelor degree) entirely taught in English (English as the language of instruction) or
  • Transcript of Studies proving at least 30 ECTS-Credits were obtained in English-taught modules.

Please note that for the latter two options you must provide a medium of instruction certificate unless the language of instruction is definitively stated on your transcript!

Proof of ability to study in German (only for international students):

Additional Documents

Further documents proving specific qualifications relevant to the desired course of study may be submitted. Examples of these additional documents are, among others, proof of additional foreign language skills, results of standardized tests specific to the fields of Business and Economics (such as GRE or GMAT) and certificates of participation in summer schools or research projects that are not documented in the applicant's academic transcript. Please note that neither a GMAT nor a GRE are a prerequisite for any of our M.Sc. programs but that they may serve as an indication that the applicant possesses a certain skill set.

Note: It is not compulsory to send reference letters. However, if you wish to hand in reference letters, you can do so voluntarily.

Recognition of International Degrees

Please note that we as the academic advisory team are not in a position to provide accurate information regarding the recognition of international degrees in Germany. These matters are handled by our central Student Affairs Office (Advising and Admission of International Students) who can provide information about the recognition of specific degrees as well as grade conversion.

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Incoming applications will be reviewed by the admission committee to determine whether they meet the entry requirements and then ranked according to the grades the applicants obtained in ther undergraduate studies and other criteria.

Timeline Admission Process

March 1

application portal opens

May 15

application deadline

June 13-20



offer letters are sent out by the end of June


start of the M.Sc. program

After the admission committee has reviewed the applications they will invite the best candidates to take part in the next stage of the admission process, the interviews. Invitations will be sent out via email at least three days before the interview and will state the day and the time of the interview. Please make sure that you regularly check your emails. Interviews are conducted via Zoom. Admitted candidates will receive an admission letter 1-3 weeks after the interview. Students who receive an offer have only a limited period of time to accept the offer and matriculate at the University of Tübingen before the offer becomes invalid and the spot will be offered to the next candidate on the list.

Student Fees
The program is free of tuition fees for all EU residents, students are only obliged to pay a small administrational fee and a contribution to the student union to matriculate.

The state of Baden-Württemberg has introduced fees for international non-EU students and second-degree students in recent years. For background information and for fee exemption options, please refer to the following link:

Tuition Fees

Further Questions?

For further questions, please contact:

Academic Advisory Service

Want to talk in person? Make an  appointment
Nauklerstraße 47 | 72074 Tübingen
+49 7071 29-76415
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Program Coordinator
Prof. Dr. Dominik Papies

Contact the Academic Advisory Service