great course

Great course

great course

Good Course

Good course

Nice Course

very nice

fantastic

very good

Loved it.

Very nice

TOO long

good one

thank's

yesss

Good!

Good!

Good

good

gfhf

Good

nice

nice

The stated goal of the course is to provide a sufficient base of knowledge in linear algebra for applied data science i.e. (a) to teach linear algebra without gory proofs or endless grinding through algorithms by hand and (b) to foreground geometric interpretations of linear algebra that can be recalled for many data science techniques and visualized with common data science tools. While I appreciate this goal and enjoyed the early foray into projection, I never felt the "a ha" moments I did as an undergrad in a class that used Gil Strang's "Introduction to Linear Algebra" (which I reread alongside this course as a supplement). The course seems to ask for some faith that various concepts introduced earlier in the course will be united by the end, but never makes good; opting instead for a kind of sleight of hand: having students implement the Page Rank algorithm with the intention that this will draw together the core concepts of the course. It could be that I was just looking for a more complete treatment of the subject than the course ever intends to offer, but I strongly felt that with a bit of restructuring, that the subject could be presented primarily intuitively, but with a level of clarity and artfulness in its conclusion that will ensure that students remember the core concepts beyond when they remember its presentation.