If you already have several advanced math courses (w/ good grades) on your transcript, then B would be the most useful. Though, you should know the material from Axler very well if you intend on doing anything that requires advanced econometrics.
I have a couple of choices of linear algebra courses to take.
- A is standard linear algebra course, uses Strang's Intro to Linear Algebra (should give context on the material)
- B is computational linear algebra, ie it has a focus on computational applications, MATLAB programming, etc. It covers the same material as course A in a sense, but probably with less focus on the theory.
- C is proof-based, abstract linear algebra, uses Linear Algebra Done Right as a textbook (for context on the material). Known to be the hardest of the three.
B seems more helpful towards possible research work, as it provides experience with computational methods that could be useful in doing RA work (and also in applying to pre-doctoral RA positions, which I'm considering). C seems the most helpful for signalling, but I don't know how valuable linear algebra is as a signal compared to other math courses (I've heard it's not) and the potential grade hit/opportunity cost may not be worth it. A is probably the least work and maximizes probability of a good grade while giving me time to focus on other math courses. Which should I take?
I guess A would be fine. You could (should?) learn the content in B in a separate numerical analysis course. I'm not sure what the value added of taking C is over A, considering adcoms may not be familiar with the fact that it is more proof-based than A (unless the name of the course is something like "Honors Linear Algebra").
There are currently 1 users browsing this thread. (0 members and 1 guests)