Your Curriculum Structure
The MME in Data and Decision Science offers a comprehensive program of core courses in machine learning, mathematical and heuristic optimization and data-driven decision making. These courses are accompanied by a wide range of elective courses offering deep-dives into specific application areas. Our courses combine demanding and cutting-edge research with practical projects and challenges. We continuously review and expand the set of electives to cover the latest trends and stay abreast of technological change.
The two-year full-time program consists of seven key building blocks, some of which can be customized to meet your individual needs and interests. You may also enrol in a German language course at no extra cost.
DDS Essentials (10 CP to be completed in semester 1): The “DDS Essentials” consist of two courses that cover and refresh relevant essentials in mathematics, statistics, algorithms and data structures.These modules lay the foundation to participate successfully in the core courses in Data and Decision Science, as well as in some of the application areas and specializations. If you can provide proof that you are proficient in the topics covered in these courses, you may apply to exchange these modules with two of the following for modules: Technology and Innovation Management, Strategic Management, Marketing Management or Entrepreneurial Management. Proof of proficiency may for example include a detailed syllabus and exam results of the courses you have covered in your previous studies. In some cases a further exam may be scheduled to demonstrate your knowledge in this area.
Data Science (15 CP to be completed in semester 1 and 2): The block “Data Science” consists of three modules. The module “Predictive Modelling” covers the fundamentals in data handling and data quality issues, predictive modeling and validation of business- and use- cases, as well as evaluation of predictions. The course “Machine Learning and Artificial Intelligence” will focus on the fundamentals and cutting edge developments in machine and deep learning. The third module is a practical exercise that complements the two lectures.
Decision Science and Optimization Technologies (20 CP to be completed in semester 1 to 3): This block consists of four modules: The module “Exact Optimization: Modelling” introduces the concepts behind building state-of-the art decision models that are able to capture the combinatorial explosion of options, including networks and linear and integer programs. The module “Exact Optimization: Algorithms” focuses on modern methods to solve these optimization models. The course covers algorithms not only for deterministic problems but also techniques from robust optimization and algorithmic game theory, which allow to handle uncertainty in the decision making process. The module “Heuristic Optimization” covers the fundamentals of metaheuristics and the challenges encountered when designing high-performance heuristics for complex planning tasks in different domains. The fourth module is a practical exercise that complements the three lectures.
Internship or Study Abroad (20 CP to be completed in semester 2): Apply your skills in an industry work placement at global enterprises such as Deutsche Post DHL, PTV Group and others, or deepen your knowledge by studying at an international university, or participating in summer schools or online courses.
Application area electives (15 CP to be completed in semester 3): Choose three of the following elective courses to gain detailed insights into specific domains:
- Optimization of logistics systems
- Advanced planning systems in manufacturing and supply chains
- Data-driven business models
- Energy and climate
Engineering Electives (10 CP to be completed in semester 3): Choose two of the following electives to gain a deep understanding in specific technology fields:
- Advanced software engineering
- Simulation of discrete event systems
- Industrial engineering and ergonomics
- Principles of Robotic Systems Engineering
Master Thesis (30 CP to be completed in semester 4): By writing a master thesis at the end of the program you demonstrate your ability to solve a methodological or analytic challenge using the knowledge acquired during the program and the methods of scientific research.
German Language Courses (two courses to be completed in semesters 1 and 3): Learning another language is an important part of a successful career. Becoming proficient in German is also an essential precondition for succeeding on the German job market, both for an internship during your studies or a permanent position thereafter. As part of the program, we offer the option to complete intensive German language courses to broaden your cultural understanding and enhance your career options. You may be able to enrol in further language courses in semester 2 and 4 at your own convenience. The Student Counsellor will help you get in touch with the relevant contacts.