AI, Data, and Drug Discovery: Harnessing Data Science in Pharmacology

This interdisciplinary course introduces students to data-driven methods and artificial intelligence (AI) tools used to understand and improve health outcomes, with a specific focus on pharmacology and drug policy. Students will explore how statistical analysis, machine learning, and predictive modeling are reshaping the landscape of pharmaceutical research, healthcare delivery, and biological discovery. Through real-world case studies, students will learn how data science can be used to uncover patterns in drug efficacy, design clinical trials, assess side effect risks, and evaluate health policy. Students will gain hands-on experience using the R programming language to analyze large datasets related to pharmacological research, including clinical trial data, prescription patterns, and pharmacovigilance databases. The course encourages interdisciplinary thinking and situates the work of data science and AI within ethical, biological, and public health frameworks. Familiarity with basic algebra and calculus is assumed.