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Вернуться к Generalized Linear Models and Nonparametric Regression

Отзывы учащихся о курсе Generalized Linear Models and Nonparametric Regression от партнера Колорадский университет в Боулдере

О курсе

In the final course of the statistical modeling for data science program, learners will study a broad set of more advanced statistical modeling tools. Such tools will include generalized linear models (GLMs), which will provide an introduction to classification (through logistic regression); nonparametric modeling, including kernel estimators, smoothing splines; and semi-parametric generalized additive models (GAMs). Emphasis will be placed on a firm conceptual understanding of these tools. Attention will also be given to ethical issues raised by using complicated statistical models. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at Logo adapted from photo by Vincent Ledvina on Unsplash...
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1–1 из 1 отзывов о курсе Generalized Linear Models and Nonparametric Regression

автор: João C

10 мая 2022 г.

T​he course is well structured and provides relevant information regarding Generalized Linear Methods and also nonparametric regression. The provided mathematical background is relevant for understanding the different methods and, even though the materials can be somehow more difficult to follow, it enhances the statistical literacy of the student. The course requires dedication to meet the timeline requirements, which increases its effectiveness. Had some minor issues along the course (e.g. autograder in programming assignments) but they were resolved with the instructor's help. I recommend the course to anyone that wants to expand their knowledge regarding regression or that are willing to be proficient at modelling data.