Johns Hopkins University 1 credit Biostatistics
Introduces practical strategies for effective AI-assisted statistical computing. Emphasizes how large language models (LLMs) and agent-based tools can be integrated into analytic workflows to improve efficiency, reproducibility, and rigor. Teaches design, structured, reliable processes for coding, debugging, documentation, data cleaning, simulation, and pipeline development using AI tools.Includes context management, agent orchestration, LLM selection, and use of Model Context Protocol (MCP) servers. Covers responsible use, including privacy protection, handling of sensitive data, sandboxing, ethics, and cost control.Draw examples from epidemiology, public health, and applied data science. Exercises use R for illustration, but all concepts generalize to any statistical language. Leaves students with a concrete workflow for AI-assisted statistical computing that they can apply immediately.
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