Suggestion and advice for courses

Hey guys, there are a few courses that I'm considering taking, but wanted your opinions on them because it's hard for me to distinguish/narrow down which ones to will be relevant and helpful in trading. Below are a list of the courses and their descriptions:

-Applied Stochastic Processes for Financial Models
Presents a mathematical background for the stochastic processes that are
widely employed as modeling tools in finance. The emphasis is on an intuitive
approach and examples rather than on proofs and mathematical rigor. Topics include random walks, martingales, Markov chains, Poisson process and other continuous time Markov chains, Brownian motion, geometric Brownian motion, and
other diffusion processes. The relevance of the considered processes to
financial modeling is stressed throughout. In particular, applications to pricing of derivative securities and to modeling of the term structure of
interest rates are discussed.

-Introduction to Stochastic Processes
This is an introductory course in stochastic processes. Presents classes of
stochastic processes, which are widely used as modeling tools in many fields
of application, including finance, economics, accounting, and actuarial science. Covers basic theory of discrete and continuous time Markov chains,
Brownian motion and its generalization, and martingales. Also discusses statistical aspects of these processes. In the final part of the course, introduces the idea of stochastic integration and develops the rules of
stochastic calculus. If time permits, also considers some stochastic differential equations.

*I don't know which of the above two to take or should I take both of them? But that seems like it might be repetitive

-Forecasting Time Series Data
The course is an exposition of time series and forecasting techniques with
emphasis on ideas, methods, and interpretations. Discusses the determination
of the best analytical model for a given problem and the application of this model in the decisionmaking process for purposes that include description, explanation, and control of time-dependent data. Illustrates all techniques with case studies and uses computer program packages as an aid for obtaining solutions. The major focus is the Box- Jenkins approach to modeling and
forecasting time series. Topics include model building, model selecting,
descriptions of timing and correlation relationships among data sets and
forecasting models, estimation, and diagnostic checking. Other topics are
seasonal adjustment, exponential smoothing models, state space models,
and nonlinear models.

*Is this course relevant and worth taking?

Okay, now I want your opinion as whether I should take the following classes in the business school Statistics dept (Stern) or the math department (Courant) at my school:

-Introduction to the Theory of Probability (Stern)
Covers the basic concepts of probability. Topics include the axiomatic defi-
nition of probability; combinatorial theorems; conditional probability and
independent events; random variables and probability distributions; expectation of functions of random variables; special discrete and continuous
distributions, including the chisquare, t, F, and bivariate normal distributions; law of large numbers; central limit theorem; and moment generating functions. The theory of statistical estimation is introduced
with a discussion on maximum likelihood estimation

-Theory of Probability (Courant)
Introduction to the mathematical techniques of random phenomena occurring in the natural, physical, and social sciences. Axioms of mathematical probability, combinatorial analysis, binomial distribution, Poisson and normal approximation, random variables and probability distributions, generating
functions, Markov chains, applications.

Now the next two courses below both seem to emphasize the "mathematics" of investment/finance, but I'm not sure if they're even necessary if I'm going to take the Stochastic processes classes:

-Mathematics of Investment (Stern)
Discusses the mathematical and technical aspects of investments. Topics
include measurement of interest and discount rates, accumulated value and
present value, annuities, sinking funds, amortization of debt, and
determination of yield rates on securities. Applications include bond evaluation, mortgages, capital budgeting, and depreciation methods.

-Mathematics of Finance (Courant)
Introduction to the mathematics of finance. Topics: linear programming
with application to pricing. Interest rates and present value. Basic probability, random walks, central limit theorem, Brownian motion, log-normal model of stock prices. Black-Scholes theory of options. Dynamic programming
with application to portfolio optimization. Students use MatLab to do simulations and solve practical problems (no prior programming experience required)

Thanks a lot for going through all that and giving me your input and advice.