典型量化金融金融硕士项目先修课要求——典型学校项目举例
1、耶鲁大学资产管理项目：耶鲁大学资产管理项目作为金融大类项目中偏量化的项目，其先修课的要求比较明确的能够帮助我们直观的了解学校审理过程中的态度和参考，从课程方面来说，如果学校看课的话，多元微积分、线性代数或者矩阵代数、微观经济学、概率论与统计、相关的编程这些如果有则最好，没有课程也不是会被彻底卡主，学校还是会通过相关的经历和学生过往对此技能的积极补充来取判断申请人是否具备基础；A strong quantitative background will serve as a helpful foundation to prepare for the Master’s in Asset Management program. Courses in multivariable calculus, linear or matrix algebra, microeconomic theory, probability and statistics, and computer programming are not required for admission, but exposure to the subject matter through academic or supplemental study will provide a highly beneficial background for the curriculum. Applicants are asked to note any previous coursework in these topics in the application so the admissions committee can identify if you may benefit from additional quantitative exposure prior to matriculating at Yale SOM；
2、圣路易斯华盛顿大学金融项目：该项目有四个方向，其中量化金融和财富管理是STEM设计，财富管理方向要求有微积分1、统计和微观经济学的课；量化金融方向虽然没有明确课程要求，但从以往申请来看，前面说到的三个课程最好要有，此外，量化金融这个方向对量化能力有特别的偏好，Successful applicants demonstrate outstanding aptitude for advanced quantitative finance materials. Students who have majored in mathematics, computer science, quantitative economics, engineering, physics and statistics are especially encouraged to apply.
3、芝加哥大学金融专业：芝加哥大学的金融硕士项目是2024年秋季新设的金融项目，从整体的课程设置上来说，是比较偏量化的，也比较偏好量化背景，对于先修课方面的说明来看，类似耶鲁，没有明确的课程必须要修到的规定，但是会有课程的倾向（线性代数、统计、多元微积分、Python、C++等）希望从过往经历中能看到量化技能掌握的证据支持，While there are no specific required classes, it is recommended applicants have taken advanced quantitative coursework and have exposure to coding. Examples include: Linear Algebra, Statistics, Multivariable Calculus, Python, C++.
4、UCSD 量化金融：统计、计量、数学应用的课程和相关软件工具的掌握；计算编程技能的应用；实用技能为主，上官相关的课程也可以作为能力的说明；Provide details regarding your experience with computational programming (for example, C++) and with statistical, econometrics and mathematical applications and tools (for example, SAS, Stata, MatLab, R, S-Plus, Mathematica).
金融工程/金融数学典型举例：
金融数学金融工程跟金融（量化金融分支）还是有区别的，金融大类整体上属于商学院的商科，金融工程/金融数学大部分情况下，还是会被归类为理科/工科，我们也可以简单理解为金融行业里面的码农岗位，相对来说，金工金数还是更倾向于理工科背景比较强的申请人，从申请层面来说，商科背景的学生跟商科中金融/量化金融会更匹配一些；
哥伦比亚大学分别在工学院和文理学院开设了金融工程和金融数学两个项目，这两个项目对先修课的要求也比较明确，通常在确定申请后，申请季当年的网申开通后，网申内也会有专门的位置需要申请人填写自己相关的课程、修习方式/平台、学分和成绩等细节内容，金融工程项目先修课要求，哥大对先修课/技能的要求很大程度上可以让我们去理解金融工程和金融数学对申请人的审理侧重点在哪里：
哥大金融工程：微积分、线性代数、概率论和统计学是基本的要求，学校对此也有说明Applicants aspiring to join the MSFE program are expected to possess a strong foundational knowledge of essential subjects such as calculus, linear algebra, probability, and statistics. Familiarity with differential equations would also be advantageous. Proficiency in coding and the ability to work with financial models, conduct scenario simulations and analyze and mine data are integral to the program. Therefore, a mastery of programming languages like Python is not just valuable but essential. It's noteworthy that the IEOR department offers programming bootcamps to bolster your programming skills.
虽然是工学院的项目，是工程的范畴，但是因为聚焦于解决金融问题，一定的金融知识和对金融领域的理解还是会更好的帮助到自己：A robust comprehension of finance concepts is indispensable. This includes a solid understanding of derivatives, risk management, portfolio theory, and the dynamics of financial markets. Immersing yourself in financial news and staying attuned to market trends can greatly enhance your perspective.The field of financial engineering has been substantially transformed by the advent of big data and artificial intelligence. It is highly recommended that you stay abreast of how machine learning and AI are being harnessed in this domain to drive data-centric decision-making. By keeping a pulse on these advancements, you can better equip yourself to contribute effectively to the evolving landscape of financial engineering.
哥大金融数学：金融数学大部分情况下会被视为数学的分支，相对来说，会更侧重在数学基础和背景上，微积分、线性代数、初等微分方程、概率论、统计、编程语言是基础必备的课程，更高阶的课程不是必须的，但是会提升申请人的竞争力，以及未来更好的适应研究生课程，比如高阶微积分和数学分析（包括测度论）。Applicants should have a very good working knowledge of calculus, linear algebra, elementary differential equations, probability and statistics, and a programming language.
Exposure to advanced calculus and mathematical analysis, including measure theory, is desirable but not required.
