Getting Started with Healthcare Data Analytics

Explore the structure of healthcare data, contrasting structured and unstructured data. See how data from EHRs, registries, and administrative datasets are linked, with Canadian data holdings and CIHI in view.

Explore healthcare data linkage, connecting records across time and systems using internal and external linkage, deterministic and probabilistic methods, and apply join types to build complete, longitudinal patient journeys.

Explore the four main medical coding systems, ICD-10, CCI, LOINC, and NDC, and how standardized codes enable precise diagnoses, interventions, lab results, and drug identification for sharing, billing, and research.

Explore data with exploratory data analysis and visualization to understand structure, quality, and patterns, address missing values and outliers, and guide cleaning, pre-processing, and feature engineering.

Explore healthcare data visualizations from categorical and continuous charts to time series, geographic maps, and relationship plots, then build interactive dashboards with Tableau, Power BI, and R Shiny.

Examine two healthcare case studies on distribution-aware data analysis, focusing on length of stay using median and IQR, and clinical trial outcomes with non-parametric tests, p-values, and effect size.

Explore hypothesis testing in healthcare analytics, including null and alternative hypotheses, one-tailed and two-tailed tests, p value, confidence intervals, and concepts of power and type I and II errors.

Compare means across two or more groups using t tests and ANOVA, then analyze associations with chi squared test for categorical outcomes, guided by assumptions and post hoc refinements.

Learn to test associations and predict outcomes using linear, logistic, and Poisson regression and GLMs, with mixed effects, confounding control, and model fit concepts.

Explore time-to-event data in healthcare with Kaplan-Meier curves, log-rank tests, and the Cox proportional hazards model, while accounting for censoring and competing risks.

Learn to tailor data insights for the clinical, operational, executive, and patient/public audiences by using relatable framing, starting with why, avoiding jargon, and translating results into actionable impact.

Apply a four-step data storytelling framework—background and objective, data exploration, critical results, and conclusion—to translate hospital analytics into actionable insights, with clear delivery and credible uncertainty.

Protect patient privacy in healthcare analytics by applying de-identification and anonymization, understanding PHI and HIPAA, and balancing data aggregation with small cell suppression.

Explore how equity in healthcare analytics addresses fairness, social determinants, and data balance through equity stratification and intersectionality to reduce disparities.

Explore career paths in healthcare data analytics, from population health to real world evidence, and master targeted job hunting strategies and specialized titles for success.

Develop healthcare data analytics skills by mastering programming in R and Python, SQL, statistics, and machine learning, plus essential soft skills and project-based experience.

Target the right healthcare data titles, tailor your resume with healthcare metrics, and build a personal brand through strategic networking and interview readiness focused on patient impact.