I'm in my first semester. I will post the relevant "quantitative courses" and if anyone can tell me if they are appropriate prerequisites for a quantitative finance program or if I need additional math courses, I would really appreciate it.

**Introduction to Mathematics for Economics**- This course is an introduction to fundamental mathematical techniques which are used frequently in Economics. The first part of the course covers some basic concepts such as sets, relations and functions, exponential and logarithmic functions, and linear and nonlinear equations. The second part of the course deals with single variable differential calculus: limits, continuity, differentiation, sequences, power series, optimization as well as definite and indefinite integrals.

**Linear Algebra**- This course applies Matrix Algebra to the modelling of Linear Business Systems. Topics include Matrices and Linear Transformations, Determinants and Subspaces.

**Mathematics for Economics**- This course introduces the students to mathematical topics beyond the high school calculus. It reviews differential calculus, then introduces topics such as basic matrix algebra, constrained optimization, comparative statistics for general function modes, and their application in economics.

**Statistics for Economics I**- This course is an introduction to descriptive and inferential statistics. Descriptive statistics consists of characterizing data sets by both frequency distributions and measures of central tendency and dispersion. Inferential statistics consists of techniques to make predictions or probabilistic statements about a whole population by studying the properties of a sample drawn from the population. Because inferential statistics depends on the probability theory, some probability laws will be studied, including the Binomial, Normal and t-distributions.

**Statistics for Economics II**- This course is a continuation of the topics covered in ECN 129, Statistics for Economics I. It includes such topics as goodness of fit tests, Type 1 and Type II errors, analysis of variance, the assumptions underlying the classical linear regression model, simple regression and multiple regression.

**Econometrics I**- This course examines what happens when economic data do not satisfy the assumptions of the Classical Linear Regression Model. It explains why ordinary least squares methods are not appropriate in the presence of, for example, autocorrelation or heteroscedasticity, and how estimation techniques have to be modified to take these problems into account. Extensive use will be made of software packages like T.S.P.

**Econometrics II**- Extends the econometric principles developed in ECN 627. Major topics include: qualitative variables, distributed lag models, single equation forecasting, simultaneous equation systems and two and three stage least squares estimation. Assignments are processed using TSP software.

**Investment Analysis II**- This course is entirely dedicated to studying derivative securities-forward and futures contracts and how they modify the risk characteristics of a portfolio, how the exchange, clearing house and marketing to market systems work, arbitrage pricing, relationships, interest rate and currency swaps and the use of various types of options contracts and their use for hedging risk.

**Financial Risk Management**- This course looks at the question of how a financial institution controls and hedges itself against all of the various risks that it faces. The course looks at liquidity management, deposit insurance, capital adequacy, credit risk management, loan securitization, interest rate forwards, futures, swaps, caps, floors and collars and how banks use these derivative products to manipulate its exposure to various types of risk. (looking over the course outline for this course, it's basically an introduction to risk management and financial engineering. it uses Hull's "Risk Management and Financial Institutions" and "Fundamentals of Futures and Options Markets")

Additionally I have experience with Excel/VBA and to a lesser extent, MATLab.