1.Princeton University
普林斯顿大学
Princeton, NJ
MS in Engineering (M.S.E.)
- Operations Research and Financial Engineering
普林斯顿大学School of Engineering and Applied Science下的Operations Research and Financial Engineering系设有Master of Science in Engineering学位，学制两年，秋季开学。该项目是为日后申请就读Ph.D的学生设立，录取率极低，申请者需要在申请前确认好意向研究领域，并联系意向导师，在申请时需提交至少一位导师的担保书，以确保对申请者的监督与担保。
申请要点
◇ 必须提交GRE分数，无最低分数标准
◇ 托福口语28分以下或雅思口语8分以下时入学将参加英语分级测试，测试未通过的学生要求进行普林斯顿大学英语课程进修
◇ 申请截止日期：12月31日
◇ 学费：$48,940/年
课程设置
Asset Pricing I: Pricing Models and Derivatives
Financial Econometrics
Statistical Analysis of Financial Data
2.芝加哥大学
Chicago, IL
MS in Financial Mathematics
芝加哥大学金融数学硕士开设于数学系下。本项目根据入学考试情况，分为5个学季（15个月），或3个学季（9个月），均为秋季学季开学，次年秋季学季毕业。学生均需修满1350个学分（包括所有的必修课和300个单位学分的选修课），依据分班考试成绩与移民身份，所需学分可以最少减少到1200个。
申请要点
◇ 不接受GMAT，接受GRE
◇ TOEFL要求90分以上， IELTS要求7分以上
◇ 建议有相关工作经验
◇ 必须有足够的数学功底，熟悉多变量微积分、线性代数和概率
◇ 最好有计算机编程基础（熟悉C++或其他基于对象的编程语言,MATLAB等），开学前未通过计算机编程分班考试的学生，还需另外完成Computing for Finance in C++课程的学习
◇ 申请截止日期：1月3日
◇学费：学分的课程：大概$6,381/门
课程设置
Q1秋季学季：
必修课：
Mathematical Foundations of Option Pricing
Computing for Finance in Python (counts toward computing requirement)
Introduction to Finance and Markets (possible to test out through placement exam; cannot be taken for elective credit)
Portfolio Theory and Risk Management I
Probability for Risk Management
Career Seminar
选修课：
Financial Mathematics Practicum
Q2冬季学季：
必修课：
Stochastic Calculus
Numerical Methods
Computing for Finance in C++ (possible to test out through placement exam; counts toward computing requirement)
Portfolio Theory and Risk Management II
选修课：
Project Lab
Financial Mathematics Practicum
Q3春季学季：
必修课：
Regression Analysis and Quantitative Trading Strategies
Fixed Income Derivatives
Advanced Computing for Finance (counts toward computing requirement)
Foreign Exchange: Markets, Products, and Pricing
选修课：
Analysis of Financial Time Series @ Chicago Booth
Project Lab
Financial Mathematics Practicum
Q4夏季学季：
选修课：
Introduction to HPC in Finance
Project Lab
Financial Mathematics Practicum
Q5秋季学季：
选修课：
Multivariate Data Analysis via Matrix Decompositions
Case Studies in Computing for Finance (can count toward computing requirement)
Topics in Economics
Machine Learning in Finance (can count toward computing requirement)
Statistical Inference for Risk Management
Mathematical Market Microstructure without Rationality Assumptions
Applied Algorithmic Trading
Regulatory and Compliance Requirements for Financial Institutions
Project Lab
Financial Mathematics Practicum
3.Stanford University
斯坦福大学
Stanford, CA
MS in Computational and Mathematical Engineering
-Mathematical and Computational Finance
斯坦福大学的工程学院下设有计算与数学工程硕士，其中包含数学与计量金融分支。秋季开学，学制两年，需要完成45学分。建议申请者本科修读过线性代数、概率、统计、数值解法、编程方面的相关课程。
申请要点
◇ 仅接受GRE分数，不接受GMAT分数
◇ 托福最低89分，建议113分以上。不接受雅思
◇ 申请截止日期：1月9日
◇ 学费：每年$52,188
课程设置
Foundational (9 units)：
Numerical Linear Algebra
Partial Differential Equations of Applied Mathematics
Discrete Mathematics and Algorithms
Optimization
Convex Optimization I
Stochastic Methods in Engineering
Introduction to Stochastic Differential Equations
Programming (9 units)：
Software Development for Scientists and Engineers
Advanced Software Development for Scientists and Engineers
Software Design in Modern Fortran for Scientists and Engineers
Introduction to parallel computing using MPI, openMP, and CUDA
Distributed Algorithms and Optimization
Parallel Methods in Numerical Analysis
Parallel Computing
Parallel Computer Architecture and Programming
Advanced Multi-Core Systems
Finance electives (9 units)：
Mathematical Finance
Financial Markets
Debt Markets
Financial Markets I
Quantitative Trading: Algorithms, Data, and Optimization
Bitcoin and Crypto Currencies
Credit Risk: Modeling and Management
Optimization of Uncertainty and Applications in Finance
Financial Statistics
Statistical Methods in Finance
Quantitative Trading: Algorithms, Data and Optimization
Data Science electives (9 units)：
Machine Learning
Modern Applied Statistics: Learning
Modern Applied Statistics: Data Mining
Mining Massive Data Sets
Natural Language Processing with Deep Learning
Statistical Methods in Finance
Quantitative Trading: Algorithms, Data, and Optimization
Practical component (9 units)：
Master's Research
Financial Risk Analytics
Credit Risk: Modeling and Management
Optimization of Uncertainty and Applications in Finance
Financial Statistics
Systemic and Market Risk : Notes on Recent History, Practice, and Policy
Big Financial Data and Algorithmic Trading
4.Columbia University
哥伦比亚大学
New York,NY
MS in Financial Engineering
哥伦比亚大学的金融工程硕士项目开设于Department of Industrial Engineering and Operations Research。分5个方向：计算与编程、金融与经济、金融衍生物、资产管理、计算金融与交易系统。秋季学期开学，需修满36个学分。
申请要点
◇ 不接受GMAT，接受GRE
◇ TOEFL 99分以下 和IELTS 6.5分以下会被要求进行语言课程的学习
◇ 最好有相关全职工作经验或者实习经验，但非必须
◇ 申请截止日期：每年1月6日
◇ 学费：17-18学年度，$69,732（$1937/学分，共36学分）
课程设置
核心课程：
Mathematics of Financial Engineering Primer
Optimization Models and Methods
Stochastic Models
Foundations of Financial Engineering
Professional Development
Monte Carlo Simulation
Financial Engineering: Continuous Time Models
Statistical Analysis and Time Series
选修课：
Quantitative Corporate Finance
Applications Programming for Financial Engineers
The Contemporary Financial Systems: Introductions for Financial Engineers
Risk Management and The Financial System
Programming for Financial Engineering
Computational Methods in Derivatives Pricing
Algorithmic Trading
Event Driven Finance
Risk Management
Machine Learning for OR & FE
Quantitative Risk Management
Asset Allocation
Beyond Black-Scholes: The Implied Volatility Smile
Big Data in Finance
Foreign Exchange & Related Derivatives
Programming for FE 2: Implementing High Performance Financial Systems
Advanced Corporate Finance
Quantitative Finance: Models and Computation
MA in Mathematics of Finance
哥伦比亚大学金融数学硕士项目开设于Graduate School of Arts and Sciences，学制分为全日制与非全日制，均为秋季入学。全日制学生2-3学期完成学业，非全日制学生必须在4年内完成学业。所有学制的学生均需完成6门必修课和12学分选修课的学习。
申请要点
◇ 接受GMAT或GRE分数，最好提交GRE分数
◇ TOEFL要求100分以上 ，IELTS要求7.5分以上
◇ 最好有金融相关实习或工作经验，无硬性要求
◇ 往届录取情况：（2017年）
申请人数：1450人
拥有相关实习或工作经验的人数占比：97%
GRE平均分：
Q169.6分，V159.8分，Analytical Writing 3.8分
GRE中位数：
Q170分，V160分，Analytical Writing 4.0分
女生比例：49%
◇ 申请截止日期：5月11日（2018年秋季入学）
◇ 学费：
$33,260/每学期（20学分以内）
$1,704/学分（超过20学分的部分）
课程设置
必修课：
Introduction to the Mathematics of Finance
Statistical Inference / Time-Series Modeling
Stochastic Processes – Applications
Stochastic Methods in Finance
Numerical Methods in Finance
Practitioners’ Seminar
5.Johns Hopkins University
约翰霍普金斯大学
Baltimore, MD
MS in Engineering
- Financial Mathematics
约翰霍普金斯大学金融数学硕士下设于其工程学院Whiting School of Engineering的应用数学与统计系。本项目只在秋季学期开学，学制为3学期，全日制。根据不同的课程结构，学生获得学位的途径可分为两条，一条是传统毕业途径（Legacy Track），一条是重点领域毕业途径（Area of Focus Track）。
申请要点
◇ 接受GRE分数，不接受GMAT分数。V部分153分以上，Q部分156分以上， Analytical Writing部分3.0分以上
◇ TOEFL不低于100分 ，IELTS不低于7分
◇ 无工作经验要求
◇ 往届录取情况：
2016年申请人数：679人
2016年录取人数：169人
2015年申请人数：590人
2015年录取人数：146人
◇ 申请截止日期：1月15日
◇ 学费：$26,085/学期
课程设置
传统毕业路径（LEGACY TRACK）
核心课：
Stochastic Processes and Applications to Finance
Monte Carlo Methods
Investment Science
Introduction to Financial Derivatives
Financial Mathematics Masters Seminar
Financial Computing Workshop
Communications Skills Practicum
Time Series Analysis
Interest Rate and Credit Derivatives
Financial Engineering and Structured Products
Financial Mathematics Masters Seminar
Internship
Applied Statistics and Data Analysis
Risk Measurement/Management in Financial Markets
Optimization in Finance
Financial Mathematics Masters Seminar
选修课：
One course in Applied Mathematics and Statistics
One course in Financial Mathematics
One additional course with prior program approval
重点领域毕业途径（AREA OF FOCUS TRACK）
金融数学核心课：
Introduction to Financial Derivatives
Interest Rate and Credit Derivatives
应用数学核心课：
Stochastic Processes and Applications to Finance
Applied Statistics and Data Analysis
Time Series Analysis
选修课：
One course in Applied Mathematics and Statistics
Two courses in Financial Mathematics
Four additional courses from the approved electives listing
风险管理方向（RISK MANAGEMENT）
Monte Carlo Methods
Financial Computing in C++
Risk Management* (required)
Quantitative Portfolio Theory & Performance Analysis
Advanced Equity Derivatives
资产管理方向（ASSET MANAGEMENT）
quity Markets and Quantitative Trading
Investment Science
Financial Computing in C++
Risk Management
Quantitative Portfolio Theory & Performance Analysis
Advanced Financial Theory
Optimization in Finance
金融衍生物方向（DERIVATIVES）
Monte Carlo Methods
Stochastic Processes & Applications to Finance II
Financial Computing in C++
Financial Engineering and Structured Products
Advanced Equity Derivatives
Advanced Financial Theory
Commodities & Commodity Markets
数量化交易/算法交易/高频交易方向（QUANTITATIVE TRADING/ALGORITHMIC TRADING/HIGH FREQUENCY TRADING）
Data Mining
Equity Markets and Quantitative Trading
Financial Computing in C++
Optimization in Finance
Statistical Theory
Bayesian Statistics
Machine Learning
固定收益与商品方向（FIXED INCOME & COMMODITIES）
Stochastic Processes & Applications to Finance II
Investment Science
Financial Computing in C++
Risk Management
Financial Engineering and Structured Products
Commodities & Commodity Markets
