As a Graduate Fellow with the DIFUSE (Data Science Infused into the Undergraduate STEM Curriculum) program, I collaborated with faculty and undergraduates to design data science modules that integrate coding and quantitative reasoning into introductory STEM courses. My project modernized an astronomy lab for ASTR 51: Advanced Introductory Astronomy at Pomona College, where students analyze archival telescope images of Uranus and its moons. Using a Python-based Google Colab notebook, students learn to model orbital motion, estimate Uranus’ mass with Newton’s Law of Gravity, and explore how uncertainty influences scientific conclusions through Monte Carlo simulations.

This fellowship challenged me to balance accessibility for students with no prior coding experience while offering deeper challenges for those with programming backgrounds. The resulting module emphasizes both data science skills and astronomical interpretation, preparing students to engage more fully in future STEM coursework.

You can explore the curriculum and access the teaching materials on GitHub: DIFUSE Astro-Imaging Module on GitHub.