Practical, enterprise-oriented learning paths for healthcare data, interoperability, knowledge representation, AI and cloud engineering. Each course has a dedicated folder, a complete Markdown syllabus, daily hands-on exercises, tool setup, assessments and references.
| Course ID | Learning path | Theme | Level | Duration | Tags |
|---|---|---|---|---|---|
| HC-KG-001 | Practical RDF, OWL, SHACL and FHIR Knowledge Graphs | Knowledge graphs / semantic interoperability | Beginner–Intermediate | 3 weeks / 30 hours | rdf turtle owl shacl rdflib graphdb fhir snomed-ct |
- Knowledge graphs: RDF, RDFS, OWL, SHACL, SPARQL, terminology integration, semantic representation.
- Interoperability: HL7 FHIR, healthcare data exchange and integration.
- Healthcare NLP: Clinical text processing, extraction and information retrieval.
- AI agents: Healthcare AI systems, orchestration, evaluation and governance.
- Cloud and DevOps: Azure, GCP, CI/CD and enterprise delivery.
- Create a new directory under the appropriate theme, e.g.
healthcare-nlp/clinical-spacy/. - Add a
README.mdwith YAML front matter containingcourse_id,title,domain,category,level,duration_hours,duration_weeks,daily_hours,tagsandstatus. - Include objectives, prerequisites, environment setup, daily syllabus, practical deliverables, assessment rubric, capstone, licensing/data safety notes and full-text reference URLs.
- Add a row to the catalog above. Use lowercase hyphenated tags and a stable ID such as
HC-NLP-001.
Use GitHub repository search for terms such as graphdb, fhir, shacl or healthcare-nlp. Course front matter provides consistent searchable metadata. GitHub does not automatically create tag filters from YAML; a generated catalog or site can be added later.
- Course directory:
<theme>/<course-slug>/ - Primary syllabus:
README.md - Course ID:
HC-<DOMAIN>-NNN - Keep synthetic datasets and exercise code within the course folder.
- Never commit patient PHI, proprietary terminology releases or credentials.