Applied Data Science &
AI Master

In the first module you’ll work through every step of the data science life cycle and get an overview of how to successfully implement data science in organisations and run projects — including the fundamentals of project management. You’ll get to know the roles, tasks and perspectives for data science in business and society.

• Data science process models
• Working in a data science team
• Overview of machine-learning methods
• Project management fundamentals
• Visualising and communicating analytical results

You’ll get to know the end-to-end data analytics process as a strategic tool for data-driven decisions — from data acquisition and advanced machine-learning techniques to communicating results convincingly.

• Process models & project planning (CRISP-DM, agile & deployment-oriented approaches)
• Data acquisition & data quality (API integration, web scraping, feature engineering)
• Advanced ML techniques (gradient boosting, ensembles, deep learning)
• Model evaluation (ROC, precision-recall, cross-validation)
• Data storytelling & data visualisation for stakeholders

You’ll learn the tools of professional software development to implement data science projects cleanly — from version control and object-oriented programming to simulations and working with online data.

• Testing & evaluating code (unit tests, integration tests, benchmarking)
• Object-oriented, procedural & functional programming
• Version control with Git & GitHub
• Working with REST APIs, web scraping & parsing HTML
• Full analysis project with online data (scraping, ETL, analysis, report)

You’ll master the core machine-learning methods and run ML projects independently — from data preprocessing and model selection to critically evaluating and explaining your results.

• Learning paradigms: supervised, unsupervised & reinforcement learning
• Data preprocessing & feature engineering (scaling, encoding, PCA)
• Classification & regression (random forests, gradient boosting, XGBoost, SVM)
• Model evaluation: metrics, cross-validation, bias-variance trade-off
• Explainable AI (SHAP, LIME) and tooling (Python, scikit-learn)

You’ll learn the foundations and methods of data engineering and build powerful, scalable data architectures — from data warehouses and ETL pipelines to DevOps principles.

• Foundations of data architecture, data lake & data warehouse
• Data integration, storage, transformation & modelling
• Building and using ETL pipelines with Python
• Applying SQL
• DevOps, continuous integration/delivery & containerisation

You’ll learn to bring machine-learning models reliably into production and operate them across their entire lifecycle — reproducibly, scalably and under continuous monitoring.

• MLOps foundations & lifecycle management
• Designing & implementing ML pipelines (e.g. with MLflow)
• Monitoring & logging: detecting model and data drift
• Automated retraining & versioning of models and data
• Testing, governance & reproducibility of ML systems

You’ll learn to deploy AI responsibly, safely and compliantly (trustworthy AI) — from ethical trade-offs and systematic risk analysis to the key European regulations.

• AI as a socio-technical system & the AI lifecycle as a governance structure
• Principles of trustworthy AI (fairness, accountability, transparency, robustness)
• Regulation: EU AI Act, NIS2 Directive, Cyber Resilience Act
• Links to data protection & standards (ISO/IEC 42001, ISO 27001)
• Governance, role and responsibility models for AI in practice

You’ll learn to prepare data analyses so they become understandable and usable for different audiences — from data storytelling and visualisation to interactive data products.

• Data storytelling: narrative structures for data analyses
• Principles of good data visualisation & chart selection
• Interactive dashboards & data applications (Python frameworks)
• User experience (UX) for data-driven applications
• Documentation & reproducibility (model cards, data dictionaries)

You’ll understand the foundations of neural networks and generative models and gain a solid introduction to deep learning and generative AI — up to transformer architectures.

• Neural networks: perceptron, MLP, CNN, RNN & backpropagation
• Loss functions, optimizers & overfitting control
• Discriminative vs. generative models (autoencoders, VAE)
• Foundations of generative language models (tokenisation, language modelling)
• Introduction to transformers, attention & scaling laws

You’ll learn to manage and govern data as a valuable corporate asset — from data collection and data strategy to enterprise-wide, low-barrier provisioning.

• Elements & frameworks of data governance
• Data architectures (data lake, data warehouse, data mesh)
• Data lifecycle management & developing data strategies
• Data stewardship, data ownership & metadata management
• Data quality, master data management, data security & privacy

You’ll enter your chosen specialization track and build the foundation for the research project that follows — choose from Data Engineering or Generative AI (GenAI).

• Deep dive into your chosen track (Data Engineering or GenAI)
• Current methods & tools of the track
• Foundation for the research project in the same semester

You’ll build a solid understanding of academic research methods and choose the right approach for your question — preparing you for the research project and master’s thesis.

• Research paradigms & quality criteria of academic methods
• Qualitative methods (interviews, case studies) & analysis
• Quantitative methods (surveys, experiments) & statistics
• Constructive research / design science (prototypes, artefacts)
• Developing research designs & critical reflection

Over four months you’ll work on an applied question in your specialization — rigorously and hands-on (12 ECTS).

• Independent research project in your chosen track
• Applying the methods from Advanced Research Methods
• Scientific treatment of a real-world problem
• Ideal preparation for the master’s thesis

In your third semester you’ll choose an elective on current topics that matches your interests (excerpt; offering varies).

• IT & Cybersecurity
• Cyber Resilience
• Visual Communication
• Design Thinking Methods: Product Development & Service Design
• Cyber Forensics
• Agile Project Management
• Agentic AI & RAG Workflow Engineering Lab
• Creative Problem-Solving & Critical Thinking
• AI Transformation in Organisations

You’ll develop, analyse and evaluate digital business strategies and apply strategic methods to concrete concepts of digital and AI-driven transformation.

• Strategic management in the age of digital transformation
• Strategic analysis of digital business models (e.g. platform economy)
• Digital corporate strategies & innovation initiatives
• Implementing digital strategies (roadmap, communication)
• Digital maturity & AI-driven solutions

You’ve (almost) completed your studies. In your master’s thesis you’ll work on a focused research question of your choice — your professors will help you shape a topic, or you can take on one we propose. The colloquium now takes place as part of your thesis phase.