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Data Analytics
Master
The Master’s programme is scheduled to start in the winter semester of 2027/28
What is planned so far:
- The Master’s programme will be taught entirely in English.
- A pre-semester programme will be offered, particularly for international students and Master’s students who do not have the necessary background in data analytics as taught in the Bachelor’s programme. This programme will include the following elements:
- Orientation & Organisation
- Academic Area (Learning & Preparation)
- Life in Germany & Cultural Integration
- Social & Student Life
- Support & Administration
- Technical & Didactic Requirements
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Here's what you can expect!
Compulsory modules – The methodological foundation
The four compulsory modules form the essential core of the programme. They ensure that you – regardless of your academic background – have a common methodological foundation in quantitative and data analysis methods.
| Modul | ID number | CP | Role in the curriculum |
|---|---|---|---|
| Quantitative Methods | WIW-KM-QTM-M-6 | 4,0 | Fundamentals of Quantitative Analysis; A Bridge Between Bachelor’s and Master’s Degrees |
| Applied Econometrics | WIW-DA-AE-M-7 | 6,0 | Causal inference & identification, OLS, IV, DiD, RDD, panel data, hypothesis testing |
| Advanced Econometrics | WIW-DA-ADE-M-7 | 6,0 | MLE, GMM, discrete choice models, Tobit, time series, quantile regression |
| Statistical Learning | WIW-DA-SL-M-7 | 6,0 | Supervised and unsupervised learning, regularisation, trees, boosting, model selection and validation |
Compulsory elective modules – Advanced methodology
The compulsory elective module allows you to specialise in the fields of data analytics, mathematics and computer science. Depending on your interests and the specialisation you choose, you can tailor your studies to focus on specific subject areas.
Data Analytics
| Modul | ID number | CP |
|---|---|---|
| Statistical Inference | WIW-DA-SI-M-7 | 6,0 |
| Bayesian Econometrics | WIW-DA-BE-M-7 | 6,0 |
Mathematics
| Modul | ID number | CP |
|---|---|---|
| Spatial Statistics | MAT-62-15-M-7 | 4,5 |
| Financial Statistics | MAT-62-13-M-7 | 4,5 |
| Mathematical Methods in AI | MAT-63-10-M-7 | 9,0 |
| AI Numerics (KI Numerik) | MAT-63-15-M-7 | 9,0 |
| Theory of Scheduling Problems | MAT-59-11-M-7 | 9,0 |
| Integer Programming: Polyhedral Theory and Algorithms | MAT-50-11-M-7 | 9,0 |
Computer Science
| Modul | ID number | CP |
|---|---|---|
| Human Computer Interaction | INF-16-52-M-5 | 4,0 |
| Machine Learning II – Statistical ML | INF-75-51-M-6 | 8,0 |
| Probabilistic graphical models | INF-76-51-M-6 | 6,0 |
| Data Science Literacy (new) | INF-new Modul | tba |
| Database Systems | INF-20-01-M-5 | 8,0 |
Specialisations (Tracks)
You can choose between two specialisation tracks, each catering to different career and research profiles. Both tracks build on the shared core and compulsory elective modules and enable you to develop a specific academic profile. The track you choose will be stated on your final transcript and degree certificate.
Track A: Economics
This track is designed for you if you are aiming for an academic career or a role in economic policy institutions, central banks or research organisations. The modules will deepen your knowledge of micro- and macroeconomic analysis, environmental economics and financial methods.
| Modul | ID number | CP |
|---|---|---|
| Economics of AI (ggf. neu) | WIW-neues Modul | tba |
| Industrial Economics (ggf. neu) | WIW-neues Modul | tba |
| Digital Platforms and Online Markets (ggf. neu) | WIW-neues Modul | tba |
| Empirical Microeconomics (ggf.neu) | WIW-neues Modul | tba |
| Topics in Applied Microeconometrics (neu) | WIW-neues Modul | tba |
| Asset Pricing and Portfolio Optimization | WIW-FM-APPO-M-7 | 4,5 |
| Machine Learning in Finance | WIW-FM-MLF-M-7 | 4,5 |
| Applications of Generative AI for Finance (neu) | WIW-neues Modul | 4,5 |
| Overlapping Generations Economies | WIW-FE-AME-M-7 | 4,5 |
| Dynamics of Financial Markets | WIW-FE-DFM-M-7 | 4,5 |
| Economics of Banking. | WIW-FE-ECB-M-7 | 4,5 |
| Choice under Uncertainty | WIW-FE-CUC-M-7 | 4,5 |
| Computational Intelligence | WIW-WIN-CIN-M-7 | 4,5 |
| Multiagent Systems | WIW-WIN-MAS-M-7 | 4,5 |
| Environmental and Resource Economics | WIW-RE-ERE-M-7 | 4,5 |
| Environmental Cost-Benefit Analysis | WIW-RE-ECBA-M-7 | 4,5 |
| Energy Economics | WIW-RE-ENE-M-7 | 4,5 |
Educational rationale: The Economics track combines a theoretical foundation (Overlapping Generations, Dynamics of Financial Markets) with modern empirical methods (Empirical Microeconomics, Applied Microeconometrics). The integration of machine learning methods into financial and environmental economics contexts ensures that students do not approach data analytics in isolation from the fundamentals of economic theory.
Track B: Business Analytics
This track equips you with the skills needed for data-driven decision-making in business. It is designed for you if you wish to pursue a career in management consultancy, logistics, finance or sustainability-focused corporate management. The track is divided into three sub-areas:
Accounting & Finance
| Modul | ID number | CP |
|---|---|---|
| Asset Pricing and Portfolio Optimization | WIW-FM-APPO-M-7 | 4,5 |
| Machine Learning in Finance | WIW-FM-MLF-M-7 | 4,5 |
| Applications of Generative AI for Finance (neu) | WIW-neues Modul | 4,5 |
Management
| Modul | ID number | CP |
|---|---|---|
| Artificial Intelligence in Business | WIW-MDT-AIB-M-7 | 4,5 |
| Recent Issues in Sustainability Management | WIW-SMG-RISM-M-7 | 6,0 |
| Theories and Instruments of Sustainability Management | WIW-SMG-TISM-M-7 | 6,0 |
| Digital Platforms and Online Markets (neu) | WIW-neues Modul | tba |
Intelligence, Logistics & Operations
| Modul | ID number | CP |
|---|---|---|
| Supply Chain Analytics | WIW-POM-SCP-M-7 | 4,5 |
| Production Analytics | WIW-POM-PPS-M-7 | 4,5 |
| Simulation and Analytics of Production Systems | WIW-POM-LAPS-M-7 | 4,5 |
| Case Studies in Operations Management | WIW-POM-FOM-M-7 | 4,5 |
| Logistics Planning under Uncertainty | WIW-LOG-LPU-M-7 | 4,5 |
| Facility Location and Network Design | WIW-LOG-SP-M-7 | 4,5 |
| Transport Logistics | WIW-LOG-TL-M-7 | 4,5 |
| Optimization Tools in Logistics Planning | WIW-LOG-OT-M-7 | 4,5 |
| Computational Intelligence | WIW-WIN-CIN-M-7 | 4,5 |
| Multiagent Systems | WIW-WIN-MAS-M-7 | 4,5 |
| Business Process Management | WIW-WIN-BPM-M-7 | 4,5 |
Cross-profile area
The cross-profile module allows you to tailor your curriculum beyond your chosen specialisation. You can choose from:
- Modules from the compulsory-elective area that have not been included there
- Modules from the compulsory-elective area of the other track
- Modules from the Bachelor’s programme in Data Analytics (provided they are prerequisites in terms of content and have not already been included)
- Modules from the specialisation areas of the Master’s in Business Administration offered by the Department of Economics
Research project
The research project serves as the key link between methodological training and academic independence. It comprises:
- Formulation of an independent research question
- Collection and preparation of suitable data
- Analysis using statistical or machine learning methods
- Interpretation of the results in an economic context
Documentation in the form of a report or a presentation
Master's degree final examination
The Master’s final examination is divided into three parts, which build systematically on one another:
| Prüfungsteil | Contents | Function |
|---|---|---|
| (1) Master's thesis | Independent analysis of a complex academic issue (marked as a separate assessment component, accounting for 50% of the mark) | Evidence of independence in terms of methodology and content |
| (2) Accompanying seminar | Interim report + active participation in discussions (unmarked, but a prerequisite for progression) | Quality assurance in the work process; feedback and academic exchange |
| (3) Final seminar | Presentation of results (marked as a separate assessment component, accounting for 50%) | Demonstration of academic communication skills and critical thinking |
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Qualifikationsprofil und potenzielle Berufs- und Tätigkeitsfelder
The learning outcomes of the Master’s programme in Data Analytics can be divided into four areas:
| Dimension | Learning objective | Courses (examples) |
|---|---|---|
| Methodological competence | Proficiency in quantitative analytical methods (econometrics, machine learning, Bayesian statistics) | Applied Econometrics, Bayesian Econometrics, Statistical Learning I & II |
| Professional expertise | In-depth knowledge of economics (economics or business administration) | Track Economics / Track Business Analytics |
| Research expertise | Independent academic research: research question, data, analysis, interpretation | Research project, Master's thesis, seminar |
| Transfer skills | Applying methods to new problems and communicating the results | Research project, case studies, colloquium |
Graduates possess a combination of expertise in economics and data science, the ability to analyse complex datasets and model decision-making processes, experience with modern methods of artificial intelligence, machine learning and statistics, as well as international, English-language communication skills. These qualifications make them particularly sought-after in the labour market, which is increasingly looking for professionals with quantitative and analytical skills and an understanding of economics. Typical employers include large companies and corporations, start-ups, technology-driven firms, international organisations, NGOs, public administrations, government departments, think tanks, as well as academic institutes and research departments.
In the business sector, for example, they can work in business analytics and data science, finance and risk management, marketing analytics, consulting and strategy development, and supply chain and operations analytics. In the public sector and at NGOs, opportunities exist in policy analytics, monitoring and evaluation, or smart city & public data management. In the fields of research, academia and further education, the programme qualifies graduates for analytical research projects, work in interdisciplinary research institutes, and PhDs in economics, business informatics or data science.