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Health Data Analyst
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Current availability
The subject-specific curriculum is authored but not yet published for completion. Learners may inspect its scope and learning design now. Start, grading, and completion remain unavailable until every listed gate passes.
- qualified review approval is required
- reviewer identity, qualification, evidence, and approval date are required
What this course establishes
- Translate an authorized health decision question into a governed data contract with precise grain, lineage, terminology, time and quality rules
- Implement and independently validate reproducible cohort, numerator, denominator, exclusion, stratification and uncertainty logic
- Deliver accessible, privacy-preserving analysis whose claims match data provenance, missingness, bias, design strength and clinical context
Entry knowledge
- Spreadsheet and introductory SQL concepts
- Basic descriptive statistics and data visualization
- Ability to follow privacy, security and clinical-governance procedures exactly
Scope boundaries
- Uses synthetic people, encounters, claims, observations, organizations and identifiers only. Learners must not access real health data, determine permitted use, re-identify records, change a clinical record, infer a diagnosis, recommend treatment, set billing/coding policy, deploy clinical decision support or make an individual care decision.
- Real work requires named data owners and qualified privacy/security, clinical, coding, quality-measure, statistical, interoperability, accessibility, legal/compliance and research/public-health authorities. HIPAA is not the only applicable rule; de-identified does not mean zero risk, and a technical data exchange does not prove shared clinical meaning.
Fastest competency route
Completion is self-paced and competency-based. Prior analytics experience shortens study only when every source, patient/encounter grain, terminology version, time rule, transformation, exclusion, denominator, missing value, access decision and result is reproducible; no completion time, credential, clinical accuracy, regulatory approval or career outcome is guaranteed.
- Pass diagnostics on data grain, provenance, terminology, time, missingness, cohort logic, measure specification, privacy, interoperability and visualization
- Audit the synthetic longitudinal dataset and reproduce every identity, duplicate, code-version, unit, timestamp, linkage, denominator, access and interpretation correction
- Complete a versioned analytic package with data contract, executable measure, independent reconciliation, equity/uncertainty analysis and accessible decision brief
Full self-paced route
- Decision question, clinical context, data model, provenance and quality
- Cohorts, measures, SQL logic, validation and equity
- Privacy, interoperability, visualization and controlled release
- Applied health-data measure and audit portfolio
Lesson and assessment map
Decision question, health-data model, provenance, and quality
Turn an authorized health question into a precise, traceable and testable data contract.
4 questions: recall, application, judgment, and error correction.
Cohorts, measure logic, validation, and equity
Implement a reproducible measure and prove that its result matches the written specification.
4 questions: recall, application, judgment, and error correction.
Privacy, interoperability, accessible reporting, and monitoring
Release the minimum necessary, correctly interpreted result and monitor it for drift and harm.
4 questions: recall, application, judgment, and error correction.
Required applied assignment
Specify, validate, and release a synthetic health measure
Use only the supplied synthetic records and fictional organization. Do not access real health information, infer diagnoses, alter records, make care decisions, establish permitted use, perform official coding/billing, deploy decision support or release row-level data. Real analysis requires authorized clinical, privacy/security, coding, measure, statistical, accessibility and data-owner review.
- Authorized decision question, clinical/workflow context, data inventory, grain/relationship map, source contract, terminology/time/null rules, profiling, quality catalog and end-to-end lineage
- Complete measure specification, versioned value sets, staged SQL or pseudocode, boundary/defect tests, expected stage counts, independent reconciliation and sensitivity analysis
- Missingness, bias, stratification, equity, uncertainty, small-cell and interpretation analysis with explicit design limits
- Minimum-necessary privacy/security plan, de-identification decision, FHIR semantic mapping tests, accessible dashboard/table, release controls, drift monitoring and correction log
Passing standard: 85 of 100 points, with substantive revision required.
Continue after this course
- Supervised analytics in a governed health, payer, public-health or quality-improvement environment
- Biostatistics, epidemiology, health informatics, database engineering, quality measurement, privacy engineering or accessible data communication
- Role-, employer-, system- and jurisdiction-specific credentials or licenses verified with official issuers; this internal course is not a clinical, coding, billing, health-information-management, privacy, security or informatics certification