Real-World Data Science for Pharmaceutical Research

The pharmaceutical industry is undergoing a massive shift as clinical trial data is increasingly complemented by data from routine clinical practice. Understanding how to analyze this Real-World Data (RWD) is now an essential skill for modern health data analysts. This text-only course guides you through the fundamental methodologies, data structures, and compliance frameworks required to translate raw clinical records into actionable Real-World Evidence (RWE). You will learn how to design observational studies, handle missing clinical information, and align your findings with healthcare decision-making standards. What you'll learn: Understand the fundamental differences between clinical trial data and Real-World Data (RWD); Map raw clinical records to standardized health data models like the OMOP Common Data Model; Analyze patient registries, electronic health records, and insurance claims databases; Apply statistical methods to control for confounding factors in observational studies; Navigate data privacy regulations and ethical considerations when handling sensitive healthcare data; Interpret real-world evidence to support drug development and regulatory decision-making. The course begins with foundational definitions of healthcare databases and RWD terminology, before moving into practical methodologies for data cleaning, cohort definition, and statistical analysis. You will progress through real-world case studies and conceptual exercises designed to simulate the daily workflow of a pharma data scientist. This course is designed for aspiring data scientists, healthcare analysts, and pharmaceutical professionals who want to transition into real-world evidence roles, with no prior clinical research background required. Start reading today to unlock the potential of real-world health data and advance your career in pharmaceutical research.