This module introduces you to the foundational concepts of descriptive statistics and their role in understanding healthcare data. You will explore how measures of central tendency, variability, and distribution shape provide meaningful summaries of patient populations, clinical characteristics, and health outcomes. Through guided examples drawn from real-world healthcare settings, you will see how descriptive statistics inform clinical decision-making, support quality improvement efforts, and highlight trends relevant to population health. By the end of the module, you will be able to compute, interpret, and clearly communicate key descriptive statistics, enabling you to identify important patterns, compare clinical groups, and generate insights from healthcare datasets with confidence.

This module introduces learners to the foundations of hypothesis testing in a clinical analytics context. They will learn how to formulate statistical hypotheses, interpret p-values and confidence intervals, and understand the role of error rates and statistical power. Building on these fundamentals, the module explores widely used hypothesis tests for comparing clinical groups, including t-tests, ANOVA, and common nonparametric alternatives. Learners also study association tests for categorical data and correlation analysis for continuous variables. Through practical clinical examples such as treatment comparisons, disease prevalence analysis, and variable relationships, this module equips learners with the statistical tools needed to assess whether observed differences or patterns in healthcare data are meaningful and reliable.

This module introduces learners to foundational regression and predictive modeling techniques widely used in healthcare analytics. Learners will begin with linear regression to analyze continuous clinical outcomes such as hospital length of stay, lab values, and healthcare costs. They then learn logistic regression to model binary clinical events and interpret key evaluation metrics such as odds ratios and ROC curves. Building on these fundamentals, the module explores core principles of machine learning and supervised modeling, including decision trees, ensemble methods, and performance validation. Learners also examine issues of model fairness, overfitting, and deployment challenges unique to healthcare. By the end of the module, they will be able to build, evaluate, and interpret predictive models that support clinical and operational decision-making.

In this capstone module, learners apply the full set of skills developed throughout the course to conduct an end-to-end analysis of a healthcare dataset. Students will clean and prepare data, compute descriptive statistics, perform hypothesis testing, and build regression and machine learning models to generate actionable clinical insights. The final project emphasizes not only technical accuracy but also clinical interpretation, communication, and ethical considerations. By completing this module, learners demonstrate their ability to independently analyze real-world healthcare data and produce evidence-based recommendations.