Should I take Statistical Principles or Honors Linear Algebra?

[idealisticz]

I am registering for graduate classes in my computer science program. My goal is to work in quantitative finance as a quant trader or analyst. I have the option of taking either Statistical Principles or Honors Linear Algebra. The following are the course descriptions.

Statistical Principles: Topics include review of distribution theory of special interest in statistics: normal, chi-square, binomial, Poisson, t, and F; introduction to statistical decision theory; sufficient statistics; theory of minimum variance unbiased point estimation; maximum likelihood and Bayes estimation; basic principles of hypothesis testing, including Neyman-Pearson Lemma and likelihood ratio principle; confidence interval construction; and introduction to linear models.

Honors Linear Algebra: Honors version of a course in advanced linear algebra, which treats the subject from an abstract and axiomatic viewpoint. Topics include vector spaces, linear transformations, polynomials, determinants, tensor and wedge products, canonical forms, inner product spaces, and bilinear forms. Emphasis is on understanding the theory of linear algebra; homework and exams include at least as many proofs as computational problems.

Which course should I take in the fall and why?

 

[bigbadwolf]

Which course should I take in the fall and why?

Probably the first. For a budding financial engineer, tensor and exterior algebras, Jordan and rational canonical forms, and bilinear forms are luxuries. Eigenvalues and eigenvectors is probably as far as he needs to go, with enough about characteristic polynomials to make sense of it.

 

[Barny]

I agree with bbw. Also I find introductory analytical courses in linear algebra almost pointless. Here is how you find the inverse of a 2x2 matrix. Here's the same for a 3x3 matrix. Here's the same for a 4x4 matrix. Yet, in the real world, one just uses Mathematica.