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院系简介
麻省理工学院斯隆商学院创办当初，只是培养工程管理方面的人才。但是在以后的几十年里，它的规模越来越大。现在，这所商学院每年招收大约三百五十名工商管理硕士生，规模和斯坦福大学商学院相似。主要的专业方向有电子商业、财经管理、信息技术管理、新技术和产品开发、战略管理和咨询以及制造业管理等。斯隆商学院的金融硕士强调培养综合性的管理性人才，对于金融/金工背景学生都可以申请，项目招收人相对巴鲁克商学院，普林斯顿和纽约大学金融数学，人数较多。
项目详解
Master of Finance（STEM）
课程设置：
· 12 months or 18 months（optional暑期实习），学分要求一样
· 7月初开课，约120名学生，2018入学季12months（62人），18months（49人）
· 3个方向：Capital Markets (资本市场)，Corporate Finance (公司金融）和Financial Engineering (金融工程)
申请要点：
· 推荐信查IP地址
· 提交网申后，1月中旬期间提交Video Statement
· 2月初集中face-to-face interview，地点可选北京或上海
· 面试半小时，两位面试官，一位负责行为面试，另一位负责申请材料追问
· 建议课程背景：Linear algebra， Calculus， Probability，Stochastic processes，Statistics/econometrics，CS: R, Python，C, C++
· 录取平均成绩（请见下图）
先修课建议：
Mathematical Background and Programing Skills
· Linear algebra:
Basic topics, including: matrix/vector notation, operations on matrices and vectors, determinants, eigenvalues and eigenvectors, quadratic forms, and systems of linear equations.
· Calculus:
Multivariable differentiation and integration, series expansions, and function approximation and maximization.
· Probability:
Sample spaces and random variables, common distributions and densities, moments of distributions, conditional probability and Bayes’ theorem, law of large numbers, central limit theorem, joint distributions, covariance, correlation, and stochastic independence.
· Stochastic processes:
Random walks, Bernoulli trials, Markov processes, basic properties of linear time series models, continuous-time processes, and Ito’s lemma.
· Statistics/econometrics:
Parameter estimation, confidence intervals, hypothesis tests, linear regression models, ordinary least squares, and likelihood principle.
· Computer literacy:
Students entering the MIT MFin program are expected to possess basic programming skills needed for processing and analyzing data. As part of the degree requirements, all students are required to sit for and pass the Programming Literacy Test. Entering students will take the PLT at the beginning of the summer term using any of the following programming languages: R or Python. Those who do not successfully pass the test will be encouraged to attend coding office hours and required to retake the PLT. Coding office hours will be held throughout the summer term.
Self-assessment
To assess the adequacy of your mathematical background, please use the following self-assessment test. If you experience difficulties in any particular area, we strongly recommend that you strengthen your skills through self-study or formal coursework prior to enrolling in the MFin program.
Self-study Resources
· Programming:
is an online interactive training and education platform in the field of data science and programming. Helpful resources to prepare for the PLT are the following: -Ang,Analyzing Financial Data and Implementing Financial Models Using R -Ruppert and Matteson, Statistics and Data Analysis for Financial Engineering -Arratia, Computational Finance
· Mathematics:
MIT OpenCourseWare provides access to many resources that may be helpful, including lecture videos, lecture notes, problem sets, exams, and solutions.
• 18.02 Multivariable Calculus (as taught in Fall 2007)*
–Lectures 1-4: some vector and matrix properties
–Lectures 8-13: partial derivatives; Lagrange multipliers
•18.05: Introduction to Probability and Statistics
•18.06: Linear Algebra (as taught in 2010)*
•6.041: Probabilistic Systems Analysis and Applied Probability (as taught in 2010)*
•6.041x: Introduction to Probability – The Science of Uncertainty
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