Management & Data Science

Masters Programme

The Masters programme in Management & Data Science is geared towards students wanting to advance their skills in the data analysis of real-world phenomena. After completion of this program our graduates have the ability to analyze massive and complex data sets, design statistical models based on the latest in information technology. The program is designed to meet the fast-growing demand for data scientists in business, public administration, and research.

Masters Information Day

On April 8, 2020 the Graduate School invites you to the Master Info Day. Take the chance to inform yourself about the Master programmes and the application formalities.
Programme and application

Contents and Idea

The rapid economic and technological transformations occurring in the global economy and the transition from an industrial to a knowledge-based economy confront corporate leaders with new challenges. The rising flood of data associated with the dynamic complexity of today’s business environment can no longer be handled by traditional methods and manager’s personal experience alone. This degree programme delivers interdisciplinary learning and research opportunities, that allows students to gain application-oriented knowledge for practical management solutions. Through the integration of management, data analysis and information systems knowledge, students will be able to develop the latest in innovative solutions for managing information-driven organizations.


Management studies as a comprehensive study element

All students in the Masters programme Management & Entrepreneurship complete management studies as an associational study element. In the process, students deepen their professional skills in the field of management knowledge in a total of three modules. Building on the first semester (“Organisation, Innovation and Strategy”), business ideas for companies and practical partners are developed within the framework of an idea competition in the second semester (“Innovation Project”). The focus in the third semester is on the current state of research on the subject “entrepreneurship”, which is particularly applicable in relation to the foundation planning phase and business plan.

Programme Structure

During the first semester, students become experts in extracting knowledge from data. They acquire a foundation in mathematics, skills in the use of data analysis tools and an understanding of the data economics. The second semester includes modules in storage and mining of massive datasets, probabilistic modeling, analyzing networks as well as forecasting and simulation. In the third semester, students undertake a challenging research project, working in teams to address questions of practical relevance with scientific rigor – often in cooperation with leading companies, focusing on data-intensive problems from different industries. In addition students deepen their knowledge of data privacy and ethics.

Two additional electives provide the possibility for further specialization that matches your professional or personal interests. Students can choose from a wide range of subjects, e.g.: Geo Information Systems, Semantic Web, Information Retrieval and Unstructured Data, Visualization and Communication, Parallelization and High Performance Computing. Alternatively, students can choose a maximum of two optional modules from other Management & Entrepreneurship majors. Students planning to study abroad are advised to schedule this for their third semester.

In the field of Management Studies, students deepen their specialist skills by completing a total of three modules. The interdisciplinary Complementary Studies component to the program also comprises three modules and provide students with a general foundation in academic and research. The special focus of the Complementary Studies allows students to familiarize themselves with methods reaching beyond disciplinary boundaries and to develop new strategies for solving problems arising in science and practice.

In the final semester, students write their Masters dissertation and attend the accompanying Masters Forum.


Career Prospects

The Masters programme in Management & Data Science prepares students to take on responsibilities involving analytical, conceptual, consulting, and strategic work. Graduates have a wide range of career options available to them, ranging from business consulting to corporate leadership position as well as specialist tracks in information-intensive organizations. Graduates are awarded a Master of Science (M.Sc.) degree.


The Masters programme in Management & Data Science is targeted at graduates with a bachelors degree in business information systems, computer science or natural sciences and also in economics or business administration. Foreign and domestic graduates with comparable degrees are welcome – the study programme is completely taught in English.


Admission requirements include a Bachelors degree (or its equivalent) and at least 60 CP in one of the following fields of study:

  • Liberal Arts
  • Humanities
  • Natural Sciences
  • Social Sciences (incl. Economics)
  • Technical Sciences

In addition to subject-specific knowledge, English language skills are required for admission to the Masters programmes. All requirements for admission have to be met before application deadline. Further information on admission requirements and the application process can be found on the website Apply

We assume that all those who wish to study Management & Data Science must have a command of at least one programming language and a solid basic knowledge of linear algebra in order to meet the requirements of the master's degree right from the start. Please download the test below and get an insight into the topics you will be dealing with during your studies.



Degree awarded: Master of Science (M.Sc.)
Application period: April 1 - June 1
Type of programme: Thematic relevance
Study places: 25
Start date: October 1
Extent: 120 CP according to ECTS
Duration: 4 semesters
Language: English
Semester contribution: approx. EUR 353