PG Diploma in AI & Biomedical Data Science in Germany

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Provides a foundation in biomedical data types, genomics basics, and clinical informatics. Students learn how genomic, imaging, and clinical datasets are structured, stored, and used in research.

Covers core AI concepts including supervised and unsupervised learning, regression, classification, clustering, and evaluation metrics. Focuses on applications in healthcare and life sciences.

Equips students with practical programming skills in Python for data analysis, visualization, and machine learning. Includes essential libraries (NumPy, Pandas, Scikit-learn) and introductory biomedical datasets.

Covers advanced statistical methods for biomedical datasets. Includes clustering, dimensionality reduction, and supervised learning techniques applied to genomics and clinical data.

Hands-on application of ML methods (regression, random forests, SVMs) to real biomedical challenges such as disease risk prediction, biomarker discovery, and patient stratification.

Focuses on deep neural networks, including CNNs, RNNs, and graph neural networks, for biomedical imaging, omics analysis, and electronic health records.

Introduces causal inference, causal machine learning, and explainable AI. Students learn how to derive cause-effect relationships from biomedical data to guide research and decision-making.

Explores biomedical knowledge graphs for integrating heterogeneous data. Students learn data modeling, semantic reasoning, and applications in drug discovery and precision medicine.

A 12-week mentored research project at the German research institute. Students apply course learnings to solve an open biomedical research problem, producing a research report and presentation.