Author(s):
Dealing with uncertainty is a natural part of the scientific process. Just like scientists, students also need to derive rigorous conclusions from data. With the advances in computing, Bayesian methods are becoming more common in data analysis. Despite being relevant across all STEM fields, Bayesian methods are taught mainly in statistics, mathematics, data science, and computer science programs. To improve access to Bayesian education at the undergraduate level, we run a program for STEM instructors across different disciplines. In this talk, we will share the details of the program, provide resources for teaching Bayesian methods, and discuss intersection of the computational and inferential thinking in data-focused classes in STEM fields.
