Healthcare data ecosystem: types of health data — clinical, administrative, financial, genomic · Key actors: hospitals, clinics, public health agencies, insurance, governments, WHO, CDC · Health information systems: DHIS2, OpenMRS, HMIS, and national health databases · Introduction to health data standards: HL7, FHIR, ICD-10/ICD-11, SNOMED CT, LOINC · Data quality in healthcare: completeness, accuracy, timeliness, consistency · Health data privacy and ethics: HIPAA, GDPR for health data, patient confidentiality · The analytics hierarchy: from raw EHR data to population health insights · Overview of tools used: Excel, SPSS, R, Python, Power BI, SQL, and DHIS2

Electronic Health Records (EHR): structure, fields, and extracting data for analysis · Hospital Information Systems (HIS): inpatient, outpatient, emergency, and lab data · Vital registration systems: birth and death registration, cause-of-death coding with ICD-10 · Survey data in health: DHS (Demographic and Health Survey), MICS, and LSMS health modules · Disease surveillance systems: notifiable disease reporting, sentinel surveillance, IDSR · DHIS2: navigating the platform, extracting data, and building indicators · Routine Health Information Systems (RHIS): facility-level data quality and completeness · Claims and billing data: ICD codes in insurance and administrative datasets · Hands-on lab: extract and clean a real DHS health dataset for analysis

Core epidemiological measures: incidence, prevalence, mortality rate, case fatality rate · Attack rate, secondary attack rate, and outbreak investigation methods · Crude rates vs age-standardised rates: why standardisation matters for comparisons · Measures of association: relative risk (RR), odds ratio (OR), and attributable risk · Study designs: cross-sectional, cohort, case-control — strengths and limitations for analysis · Life tables and survival analysis: Kaplan-Meier curves and log-rank test · Outbreak investigation: steps, spot maps, and epidemic curves · Disease burden metrics: DALY (Disability-Adjusted Life Year), QALY, YLL, and YLD · Calculating and interpreting epidemiological measures in Excel and R · Hands-on lab: conduct a complete outbreak investigation analysis on a simulated dataset

Understanding clinical trial data: phases, endpoints, and outcome variables · Patient demographics analysis: age, sex, comorbidities, and risk stratification · Clinical outcome measures: mortality, readmission rates, length of stay, complications · Laboratory data analysis: reference ranges, abnormal values, and clinical interpretation · Vital signs trends: blood pressure, temperature, pulse, oxygen saturation — time series · Survival analysis in clinical data: Kaplan-Meier and Cox proportional hazards in R · Risk adjustment: controlling for patient case mix in hospital performance comparison · Diagnostic test evaluation: sensitivity, specificity, PPV, NPV, ROC curves, and AUC · Drug safety analysis: adverse event reporting and pharmacovigilance basics · Hands-on lab: analyse a clinical dataset — patient outcomes, readmission predictors, and survival

Principles of health data visualisation: choosing the right chart for clinical and public health data · Disease mapping: choropleth maps, dot maps, and spatial clustering in R and Power BI · Epidemic curves: constructing and interpreting epi curves by date of onset · Health dashboards in Power BI: KPIs, trend lines, and drill-through for health managers · DHIS2 dashboards: building real-time health facility dashboards from HMIS data · Visualising mortality and survival: Kaplan-Meier plots, funnel plots for hospital comparison · Demographic pyramid charts: population age-sex structure and disease burden · Health scorecards: balanced scorecard frameworks for health system performance · Communicating health data to non-technical audiences: ministers, donors, and communities · Hands-on lab: build a complete public health surveillance dashboard in Power BI

Health information system architecture: data collection, transmission, storage, and use · DHIS2 in depth: metadata, organisation units, data elements, indicators, and validation rules · OpenMRS: open-source EHR for low-resource settings — data extraction and analysis · Interoperability: HL7 FHIR APIs for connecting health systems and extracting data · Mobile health (mHealth): ODK and KoboToolbox for community health worker data collection · Telemedicine data: patient records, consultation logs, and outcome tracking · AI and machine learning in healthcare: clinical decision support, diagnosis prediction, risk scoring · Digital health ethics: algorithmic bias in clinical tools and equitable AI in health · Introduction to genomic data: basics of variant analysis and precision medicine data · Hands-on lab: connect to a FHIR API sandbox and extract patient data for analysis

Burden of disease analysis: global, regional, and national estimates from GBD, WHO GHO · Health equity analysis: disaggregating health data by sex, age, income, geography, and ethnicity · Programme evaluation in health: measuring impact of vaccination, nutrition, and malaria programmes · Health system performance metrics: access, quality, efficiency, equity, and sustainability (WHO framework) · Writing a public health analytical report: structure, evidence, limitations, and recommendations · Policy briefs for health ministries and donors: WHO, USAID, Global Fund formats · Capstone project: choose a health dataset (DHS, DHIS2, or clinical) — conduct a full analysis including cleaning, epidemiological measures, visualisation, and a written public health report

What is DHIS2? Architecture, global adoption across 73+ countries, and Nigeria NHMIS context · DHIS2 interface: navigating apps — data entry, analytics, maps, dashboards, and event capture · Organisation unit hierarchy: national, state, LGA, facility — understanding the DHIS2 structure · Data elements, indicators, data sets, and periods: the building blocks of DHIS2 data · Data entry in DHIS2: aggregate data entry, validation rules, completeness, and timeliness monitoring · Importing data into DHIS2: Excel, CSV, XML, and JSON/ADX data exchange formats · DHIS2 analytics app: pivot tables, charts, and dashboards for health managers · DHIS2 data visualiser: bar, line, gauge, and combined charts for facility and district data · DHIS2 maps app: thematic mapping of health indicators — choropleth and bubble maps by district · Data quality tools in DHIS2: completeness/timeliness dashboard, min-max outlier detection, and consistency checks · DHIS2 Tracker: individual-level data — antenatal care, immunisation, and case-based surveillance · DHIS2 Android Capture app: offline mobile data collection for community health workers · DHIS2 Web API: extracting data in JSON/CSV for integration with Power BI, R, Python, and Excel · DHIS2 in Nigeria and Africa: NHMIS, state HMIS configurations, and real-world implementation challenges · Hands-on lab: build a complete district health dashboard in DHIS2 — import data, create indicators, build visualisations, and share with a management audience

Analyse clinical, epidemiological, and population health datasets using industry-standard tools · Apply health data standards (ICD-10, FHIR, HL7, SNOMED CT) in real healthcare analytics projects · Build disease surveillance dashboards and conduct outbreak investigation analysis · Use survival analysis, risk adjustment, and diagnostic test evaluation in clinical data · Build professional DHIS2 dashboards and extract health data via the FHIR API · Produce public health reports and dashboards for health ministries, NGOs, and donors · Achieve a credential aligned to WHO, CDC, HIPAA, DHIS2, and HL7 FHIR standards

Complete all 8 modules, pass a proctored examination (minimum 75%), submit a healthcare data analysis project including a disease surveillance dashboard, clinical outcome analysis, a DHIS2 dashboard, and a 1,500-word public health report.

Healthcare Data Analyst, Health Information Officer, Epidemiologist (Junior), Public Health Analyst, Clinical Data Manager, Health Intelligence Officer, Hospital Performance Analyst, WHO/UNICEF/CDC Data Analyst. Average starting salary: $55,000–$95,000 USD.