Researchers increasingly rely on Python not just for collecting and preparing data, but for the broader ecosystem of tools that make that work faster, more reproducible, and easier to share. This course focuses on that ecosystem: the modern tools that academic researchers and data scientists use every day, taught in a way that is accessible to participants with no prior Python or programming experience.

We will begin with the Python foundations needed to work confidently with the tools that follow. From there, we will dig into Polars, a modern and high-performance alternative to Pandas for working with tabular data, with particular strengths for larger and more complex datasets. We will also spend time on tools for keeping research projects reproducible, well-documented, and organized, including version control with Git and GitHub, reproducible computing environments, and modern project and environment management. We will wrap up with Quarto, a powerful tool for creating manuscripts, presentations, and other documents that embed live code, so that tables, figures, and results are generated directly from your data and update automatically. Notably, most of the tools and workflows we cover are applicable beyond Python, including to R-based research.

No prior knowledge of Python or programming is required; the course is designed to bring all participants to a common foundation before we dive into the tools that are its focus. Participants who have completed Introduction to Python for Research will find the Python basics segments a familiar refresher, while the focus on tools will make the overall course about 80 percent new content.