Advanced Linear Models for Data Science 2: Statistical Linear Models
Johns Hopkins University · View on Coursera ↗
Taught by Brian Caffo, PhD
Math and LogicData Science
About the course
Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: - A basic understanding of linear algebra and multivariate calculus. - A basic understanding of statistics and regression models. - At least a little familiarity with proof based mathematics. - Basic knowledge of the R programming language.
The dissent
The most-upvoted critical review (2 votes, 3.0★).
“Lectures are too esoteric without enough application. There are a couple references to follow-on courses that would hopefully have some "so what" material... But no sign of this so far, and it's been 5 years.”
Coursera's 4.6 comes from 109 ratings. Of those, 4.6% are 1–2★.
Starisk reads the reviews learners voted most helpful, and weights each by the square root of its votes: a review with 80 upvotes counts about five times one with 2. Unvoted reviews are not skipped out of laziness — by this model they carry no evidence that other students endorsed them.
That measures what the most-endorsed reviews say, not “true quality.” The rating distribution above is the whole population, so you can see the sample next to it.
Reviews read on 23 Oct 2026.Read them on Coursera ↗