Data Science and Machine Learning (M.Sc.)

Theory: Statistical learning, probabilistic modeling, optimization, and generalization.

Practice: Deep learning (vision/time-series), geospatial or bio/med data applications, feature engineering, and evaluation beyond accuracy (calibration, fairness).

Engineering: Reproducibility, versioned data/models, basic MLOps, and experiment tracking.

Interdisciplinary Work: Projects with domain labs (e.g., environmental, biomedical) using real datasets and constraints.

Ethics & Safety: Bias, privacy, and model risk management; transparent reporting.

Capstone/Thesis: Publishable or production-ready project with code, documentation, and error analysis.

Outcomes: Applied scientist/DS roles, research assistantships, PhD pipelines.