Introduction to Linear Models and Matrix Algebra

(Note, I took these courses before the recent reorganization. I believe the material for the first few courses is the same, so my comments should still be valid.)

This is the second PH525 sequence course offered through HarvardX. Most of the course is dedicated to demonstrating mathematical operations for performing matrix manipulations and calculations with R. Most of what is shown can be done more easily using built-in functions in R (ex: lm()), but there is still some good information here. The descriptions of collinearity, interactions, and the demonstration of building a multivariate linear model to compare treatments and interactions in particular were better than in the dedicated statistics courses I took as a graduate student.

The programming assignments are in general much easier than the material covered in the videos, and as with the first course there is a fair amount of hand-holding throughout the assignments, which is a major difference from similar courses such as the Hopkins Data Science sequence. This is a two-week course, and since I took it self-paced, it took me only a few hours (spread over two days) to finish it. Despite my 100% score, I still feel I could learn more by repeating this course down the road, and this is probably a side-effect of the ease of the homework assignments compared to the course material. There is also a lot of instances of "here is some advanced code we're using to demonstrate how this works, but we're not going to teach you how to code this", which I found to be frustrating at times. Complete .Rmd files are available for each lecture, though, so students can go through the code independently.

Overall, four stars. Maybe not the most effective use of time if all you want to do is be able to run linear regression analyses in R, but there is a lot of good information here.