📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of AI in Personalized Medicine, from foundations to a certified capstone project.
Foundations
• Stratified, precision and personalised medicine distinguished honestly
• Biomarkers: prognostic versus predictive, and why the distinction decides use
• Evidence thresholds before a marker changes treatment
Data
• Combining genomic, transcriptomic and clinical data at patient level
• Batch effects, missing modalities and cohort heterogeneity
• Longitudinal records and treatment-response labelling
Modelling
• Subtype discovery and the instability of unsupervised clusters across cohorts
• Response prediction and the confounding of treatment assignment
• Survival modelling with censoring handled correctly
Therapeutics
• Star-allele calling and CPIC-guided dose adjustment
• Drug-gene and drug-drug interaction in clinical decision support
• Companion diagnostics and their regulatory coupling to a therapy
Delivery
• Ancestry representation and the portability of markers across populations
• Cost-effectiveness and reimbursement for stratified therapy
• Clinician communication of probabilistic, individualised results
Earn government-registered certification in AI in Personalized Medicine
e-Certificate and e-Marksheet issued on successful completion.
