No prior knowledge is required.
Students acquire the knowledge and develop the skills listed below:
1. Understand the stages of the data lifecycle, from creation to deletion or storage.
2. Use ontologies and knowledge graphs for medical language normalization.
3. Implement data anonymization techniques to share data securely.
1. Data lifecycle and quality assessment: stages, quality dimensions, FAIR principles.
2. Data governance and policies: ethical, legal, and institutional conditions, data ownership, permissions, data access committees.
3. Medical language normalization: ontologies, SNOMED CT, LOINC, HPO, interoperability.
4. Data sharing: anonymization techniques, aggregated and federated models, cloud-based environments, synthetic data generation, federated discovery, federated learning.
The classes of the Applied Data Science in Biomedicine course aim to enhance the active learning of the student, which is eminently practical. The student is an active member of the classes and learns as they develop the tasks presented with their laptop. The classes are focused on having the student code scripts, combining theoretical material with practical classes. Additionally, there will be an introduction to the R programming language, which will be combined with activities developed using Python, providing the necessary tools to successfully complete the course.
See the electronic folder of the course.
See the electronic folder of the course.
See the electronic folder of the course.
See the electronic folder of the course.