Please give an opinion of this program's strength as related to trading/hedgefunds/money management

First time poster here, please offer some objective opinions, would be great help.

I understand that many here have done really well and made the right choices in their education to ensure landing that job or career they want. It isn't the case for me and I'm trying to do some catch up. So I'd like to ask those in the know to shed some light on a program I am considering.

May I add:

• I did an undergrad in finance from an "ok" state school
• I can not get into highly quantitative programs due to lack of math background, short of taking 8-10 pre-req's, which I would rather avoid due to cost and time issues
• I am currently working in the finance world, but it is not relevant experience (which could change soon)
• I can not afford to move away, nor go full time (rather, I will be looking for nights and weekends classes)
• I do not expect to climb to the top of the Wall Street, nor do I need to for personal happiness, but I do hope for a lucrative career in trading, some portfolio management position, etc.

All these things taken into consideration, being from Chicago, the program that stands out to me is Illinois Institute of Technology, MSF with concentrations in Financial Engineering and Financial Programming.

I will here lay out course descriptions of the program I would be taking, I would really appreciate the input of some of you experienced and well learned folks on the structure of the program, the skill-set it offers, its relevancy to the key career interests I laid out, and any other general opinions. (Please don't tell me to go kill math at a community college, apply for Stanford, knock it out, make 20 mil a year :P)

Note, I am unconcerned with the name of the school (I got a job from a much bigger no-name undergrad, I will make my own fortunes again), please comment strictly on the structure and level of knowledge one would have after completing this degree (and after having kicked behind and studied hard).

Course descriptions (first 6 are the core, and rest is concentrations):

1. Mathematics with Financial applications:

This course provides a systematic exposition of the primary mathematical methods used in financial economics. Mathematical concepts and methods include logarithmic and exponential functions, algebra, mean-variance analysis, summations, matrix algebra, differential and integral calculus, and optimization. The course will include a variety of financial applications including compound interest, present and future value, term structure of interest rates, asset pricing, expected return, risk and measures of risk aversion, capital asset pricing model (CAPM), portfolio optimization, expected utility, and consumption capital asset pricing (CCAPM).

2. Statistical Analysis in Financial Markets:

This course presents the major conclusions of the econometric techniques used in finance. Ordinary least squares, maximum likelihood, generalized method of moments, and simulation methods are covered. These tools are presented through computer simulation of the various models, followed by detailed analysis of the distributions of estimators. Hypothesis testing is covered in detail. Particular attention is placed on the properties of various estimators when model assumptions do not hold. For students who qualify, a final project applying econometrics to a financial modeling problem may be chosen. Students not familiar with matrix algebra and elementary statistics should plan to make up the deficit early in the course. Additional lectures will be provided for these students.

3. Financial Modeling:

Financial modeling in a spreadsheet environment is a pervasive feature of the modern workplace. In this course, students will learn how to implement financial models, using spreadsheet modeling and basic programming, via Microsoft Excel, VBA and Matlab. Financial models will include project valuation, bond pricing and hedging, option pricing via binomial trees and portfolio optimization. The course will also cover basic numerical techniques that are essential to financial modeling, including Monte Carlo simulation, root-finding and linear algebra.

4. Valuation and Portfolio Management:

The course is a survey of asset pricing theory. The fundamentals of bond and option pricing are covered as well as the CAPM, APT and the Fama-French models. Excel spreadsheet modeling is used to illustrate and understand the concepts of Markowitz's Mean Variance Optimization, equity valuation, option pricing, and utility theory. The courses places a special emphasis on the relationship between macroeconomic conditions and investment opportunities.

5. Futures, Options, and OTC Derivatives:

This course provides the foundation for understanding the price and risk management of derivative securities. The course starts with simple derivatives, e.g., forwards and futures, and develops the concept of arbitrage-free pricing and hedging. Based upon the work of Black, Scholes, and Merton, the course extends their pricing model through the use of lattices, Monte Carlo simulation methods, and more advanced strategies. Mathematical tools in stochastic processes are gradually introduced throughout the course. Particular emphasis is given to the pricing of interest rate derivatives, e.g., FRAs, swaps, bond options, caps, collars, and floors.

6. Financial Statement Analysis:

After reviewing the content of the major financial statements, the course examines ratios, inventories, long-lived assets, income taxes, debt, leases, and pensions, among other topics. U.S. practices are compared to practices in other major countries. This course is intended for those who will examine financial statements of outside organizations.

7. Models for Derivatives:

In this course, students will learn mathematical and computational methods that are applicable to the pricing and risk management of derivatives. These will be implemented in Matlab. The class will include an introduction to option pricing theory, with some basic stochastic calculus, the Black-Scholes partial differential equation, risk-neutral valuation and hedging and portfolio replication. We may also touch on more advanced topics, such as jump processes and stochastic volatility. The course will focus on important numerical techniques used in finance, including variance reduction techniques in Monte Carlo Simulation, finite difference methods applied to partial differential equations, interpolation procedures (e.g. splines) and optimization theory applied to model calibration. These methods will be applied to price exotic options and to model volatility surfaces.

8. Interest Rates, Term Structure and Credit Models

Upon completion of this course, students should know the strengths, weaknesses, appropriate uses and ways of implementing the major term structure models that are in common use. The course will cover bootstrapping of forward curves, principal component analysis and review basic fixed income derivatives (swaps, swaptions, caps and floors). We will then implement short rate models, such as Ho-Lee, Black-Derman and Toy, and Extended Vasicek/Hull-White, followed by the Heath-Jarrow-Morton model and market rate models. The course will conclude with a brief introduction to credit modeling, covering risk-neutral default probabilities, credit default swap pricing, and structural and reduced-form models of credit risk. Students will implement these term-structure models in Excel/VBA and Matlab.

9. Computational Finance

Because of the widespread adoption of computer trading platforms, the computational efficiency of financial models has become an issue of increasing concern. This course concentrates on numerical techniques for pricing derivatives found in modern markets. It includes an extensive treatment of numerical solutions of the Black-Scholes equation, using techniques such as efficient binomial/trinomial trees, finite-difference solutions of partial differential equations and Fast Fourier transforms. We will cover optimization theory as used in model calibration. We will apply these methods to various pricing models, such as stochastic volatility models and models used to price credit derivatives. Model implementation will be in Matlab.

10. .NET and Database Management
    The course provides students with a comprehensive knowledge of .NET (VB and C#) programming, relational database design and SQL as they apply to quant finance and real-time trading. Specifically, topics covered include the .NET framework and libraries, ADO.NET, OOP, generics, market data feeds, XML and the Unified Modeling Language, as well as an overview of the hardware and network infrastructure necessary to enable electronic trading.

11. C++ with Financial Applications
    This course presents the C/C++ programming language. Students learn the language from the ground up, from data types, to functions, arrays, classes, dynamic memory management, data structures and the Standard Template Library. Object-oriented programming is also discussed, including a review of commonly used design patterns. The focus is to understand C/C++ as it applies to financial mathematics and several practical examples from computational finance are presented.

12. OOP and Algorithmic Trading Systems
    In this course, students learn advanced programming topics in .NET for real-time financial applications and automated trading systems, including multithreading, sockets, APIs, synchronization, the FIX and FAST protocols, and object oriented design for event-driven applications. Also, project management and software quality are covered in depth. Lastly, topics related to latency in real-time financial applications and alternative network architectures are also discussed. Students are expected to propose, design, document and develop an original project combining concepts from quantitative finance and trading strategy (presented in other courses) into a working software application.

Apologies for the length of the tread, but I felt it necessary to include the details of these courses.

Your input is very much appreciated. Any takers? Illini? Help a hometown brotha?

Thanks