In this final course of the statistical modeling for data science program, learners delve into advanced statistical modeling tools, including generalized linear models (GLMs) and nonparametric regression techniques like kernel estimators and smoothing splines. They also explore semi-parametric generalized additive models (GAMs), emphasizing a solid conceptual grasp of these tools. Ethical considerations in utilizing complex statistical models are also addressed. Offered as part of CU Boulder’s Master of Science in Data Science (MS-DS) program on Coursera, this course integrates expertise from various departments, fostering an interdisciplinary approach. Students can earn academic credit through the MS-DS, a program designed to meet the evolving demands of data science. By mastering advanced statistical techniques, learners gain valuable skills for analyzing complex data and making informed decisions. Through a focus on both practical applications and ethical considerations, this course equips students with the expertise needed to excel in data science roles