Statistical Computing for Health Care Research with R

Modern healthcare research relies heavily on data, yet turning raw clinical or public health data into actionable insights requires specialized computing skills. This course bridges the gap, introducing you to the fundamentals of statistical programming tailored specifically for health and medical data. By the end of this text-based program, you will transition from manual data handling to writing clean, reproducible R code for advanced statistical analysis. You will gain the confidence to handle clinical datasets, run robust simulations, and apply modern machine learning techniques to real-world healthcare scenarios. What you will learn: Learn foundational R programming concepts, including tidy data principles and data manipulation; Understand how to perform simulation and bootstrap methods to estimate clinical parameters and test hypotheses; Apply optimization techniques to fit statistical models to complex healthcare data; Implement basic machine learning algorithms to predict health outcomes and identify patient risk factors; Practice writing reproducible research reports using modern markdown-based documentation workflows. The course begins with core statistical terminology and fundamental R programming syntax, building step-by-step toward advanced simulations and predictive modeling. Through written explanations, structured code walkthroughs, and practical exercises, you will master the analytical workflows used by top health researchers today. This course is designed for health care professionals, clinical researchers, and public health analysts who are new to programming. No prior coding experience is required. Start learning today and unlock the power of statistical computing in your health research.