3.0

Average Course Rating

This course equips students with the dual expertise required for the next generation of clinical research: cutting-edge AI engineering and rigorous clinical data science and pharmaceutical statistics. The curriculum begins with a technical grounding in Generative AI, covering essential topics such as prompt engineering, Retrieval-Augmented Generation (RAG), fine-tuning, and the architecture of autonomous agents. This phase ensures students can go beyond simple prompting to build sophisticated, domain-aware systems. In parallel, students will learn foundational statistical methods in clinical data science including survival data analysis, longitudinal data analysis, clinical trial designs, and the use of real-world data. The final phase of the course is devoted to "The Agentic Biostatistician," where students build and deploy AI agents designed to automate the daily tasks of clinical data science. Performance in this course is evaluated through a project-based curriculum centered on building agents. Students are required to design, develop, and deploy their own AI agents to solve specific challenges in clinical data science with the help of the instructor and TA.