Description
Healthcare and life-science organizations generate vast amounts of real-world data-from electronic health records and claims to registries, device data, genomic profiles and patient-reported outcomes. Yet the true value of this data often remains unrealized. This certificate helps decision-makers understand what these data can (and cannot) tell us, how to choose and trust analytic methods, and how to translate results into sound decisions. Drawing on real healthcare examples, this certificate emphasizes the why, when, and how of data science in health: why a particular analytic method is appropriate for a given research question, when its results should (and should not) be trusted, and how findings can be interpreted to inform real-world decisions in clinical, regulatory, and commercial settings.
Applying
Who Should Enroll
Designed for professionals in:
- Pharmaceutical and biotechnology companies
- Health systems, payers, and policy organizations
- Medical device and diagnostics firms
- Government and regulatory agencies
Ideal for roles such as:
- Health economics and outcomes research (HEOR) and market access leaders
- Real-world evidence (RWE) analysts
- Medical science liaisons
- Product and strategy managers
- Data scientists and data science collaborators
- Graduate-level researchers
This certificate is especially relevant if you manage data scientists, collaborate with analytics teams, or make evidence-based decisions. You'll better assess credibility and communicate findings effectively.
Competencies
What You'll Learn
- How to evaluate real-world data sources like claims, electronic health records (EHRs), and registries
- How causal inference supports comparative effectiveness
- When to trust regression, machine learning, or hybrid approaches
- How to choose covariates and interpret outputs
- How data science supports policy, pricing, and access decisions
- How to frame meaningful research questions and understand how methodological choices shape conclusions