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Value of Financial Computing MSc at QMUL

Hi All,

I've been looking at the Financial Computing MSc offered at Queen Mary in London, and thinking about what are the prospects for a career in finance?
The website states that the university collaborates with partners such as Citigroup, Nomura, Morgan Stanley etc. who provide teaching activities, guest lectures and collaboration on MSc projects
What kind of roles could a graduate from this course get and how easily?

Here is a summary of the key course content, with the electives that I would choose:

Advanced Program Design:
- object oriented programming in Java
Foundations of mathematical modelling in finance:
- introduction to probability theory and stochastic processes used in modelling asset price dynamics.
stochastic processes:
random walks, Brownian motion, Poisson process. Stochastic calculus
Topics in scientific computing:
solving applied mathematical problems: search algorithms, generating network ensembles, numerical solution of differential equations, random number generation.
Elective: Machine learning:
Introduction to machine learning methods: pattern recognition, clustering, neural networks

- Financial programming:
Develops skills in Excel, VBA and C++ used in a "three tiered architecture"
- Advanced computing in finance:
Further development in C++. Use of Monte Carlo simulations for pricing options. Black scholes theory and its connection to PDEs + investigation of models beyond Black-Scholes theory based on stochastic volatility
- Portfolio theory and Risk management:
learn skills to apply modern risk measures and portfolio management tools. Maximization of expectation of suitability functions which characterizes the optimum portfolio. Theoretical background of optimization schemes.
- Stochastic calculus and Black Scholes theory:
Acquire deeper knowledge about Ito stochastic calculus. Role of Ito integral in solving stochastic differential equations and its role in developing the Black-Scholes theory for option pricing. This course will develop pricing methodologies for both vanilla options as well as exotic options such as barrier options.
The content of the two courses are actually very similar. I'm a physics graduate and am currently working in data processing so i'd prefer a more computing oriented programme, however I have already been made an offer and could probably switch to Mathematical finance.

So what do you think of these two courses?

Thanks for any insight you can give me.