Generalized linear models and nonparametric regression

In the capstone course of the Statistical Modeling for Data Science program, students will explore a broad set of more advanced statistical modeling tools. These tools will include generalized linear models (GLM), which will introduce classification (via logistic regression); nonparametric modeling, including kernel estimators, smoothing splines; and semiparametric generalized additive models (GAMs). Particular emphasis will be placed on developing a solid conceptual understanding of these tools. Ethical issues that arise when using complex statistical models will also be addressed. This course can be taken for credit toward CU Boulder’s Master of Science in Data Science (MS-DS) program, offered on Coursera. The MS-DS is an interdisciplinary degree that includes faculty from CU Boulder’s Applied Mathematics, Computer Science, Information Science, and other departments. The MS-DS is a learning outcomes-based, no-application program that is ideal for individuals with a broad educational background and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from a photo by Vincent Ledvina on Unsplash