The Department of Mathematics is part of the Courant Institute of Mathematical Sciences, an independent division of New York University. Based in Warren Weaver Hall, at the heart of New York University’s Washington Square campus, it is one of the top-ranked Mathematics departments worldwide (#4 in the US and #9 in the world as per the Shanghai rankings; #6 in the US and #8 in the world as per the QS rankings).
The Master's degree in mathematics encompasses the basic graduate curriculum in mathematics, and also offers the opportunity of some more specialized training in an area of interest. A typical Master's course of study will involve basic courses in real analysis, complex analysis and linear algebra, followed by other fundamental courses such as probability, scientific computing, and differential equations. Depending on their mathematical interests, students will then be able to take more advanced graduate courses in pure and applied mathematics.
Students complete their Master’s Project in the “Project & Presentation” course. They conduct research in smaller groups (2-3 students), supervised by faculty, adjunct faculty members, and/or subject matter experts in the financial industry. Students produce a written report (Master’s thesis) and give a final presentation; each contributing equally to their final grade.
Upon successful completion of the program, graduates will have acquired:
Development of computer software skills, including facility with a high- level language such as Python, and the ability to work with financial databases.
Development of mathematical skills, including the tools from probability, statistics, and scientific computing that are most useful in quantitative finance.
Development of a broad understanding of financial markets and the many investment instruments they encompass.
Development of familiarity with widely-used financial models for pricing, hedging, risk-management, asset allocation, and other applications of quantitative finance. This includes understanding the models' hypotheses and limitations.
Acquisition of specialized skills associated with selected quantitative career paths; examples of such skills include algorithmic trading, statistical arbitrage, and financial machine learning.
Acquisition of "soft skills" that are crucial for placement and career advancement, including the ability to network effectively, the ability to interview well, and the ability to work well as part of a team.