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 https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Vincent Ledvina on Unsplash
Can speak highly enough of this professor. He is extremely knowledgeable and can convey concepts in one of the clearest ways I have ever seen in my academic career.
The pace of instruction is excellent and the assignments make it easy to translate theory to practice.
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Course is ok but the peer-graded review system is a colossal pain. In my opinion, the peer-graded assignments do not add any value and it is a constant struggle to get timely and fair reviews.
"Excellent course to delve into the assumptions of the generalized linear model and, at the same time, learn the R programming language."
The pace of instruction is excellent and the assignments make it easy to translate theory to practice.
The 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.
Can speak highly enough of this professor. He is extremely knowledgeable and can convey concepts in one of the clearest ways I have ever seen in my academic career.
High quality course. It is not for beginners.
Brian Zaharatos is a great professor!
The material is not presented well at all - I've taken many different similar courses and this was likely the worst in terms of how the material was taught. Further more, there are broken links (the ethics article in the first peer-reviewed model), and there are files that won't load in some of the labs & issues with the R versions. Is it too hard to maintain this given it's a requisite for CU's MS-DS? Also, the peer reviews and ProctorU need to go. Peer reviews are just lazy - these people are taking the same course I am, there's nothing to say they're qualified to review and grade my work - that became very evident this course when I was given completely incorrect feedback on an assignment, and had to resubmit (the exact same assignment) and received a great grade.