Master of Finance
Suggested Background
The Master of Finance (MFin) program at the MIT Sloan School of Management is quantitatively rigorous. Applicants are not required to have a specific undergraduate major or complete a prescribed set of courses, but preparation in linear algebra, multivariable calculus, probability, statistics or econometrics, and programming will help students succeed in the curriculum. Use the self-assessment and preparation resources below to identify areas you may want to strengthen before enrolling.
Mathematical Background and Programming Skill
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Linear algebra
Basic topics, including: matrix/vector notation, operations on matrices and vectors, determinants, eigenvalues and eigenvectors, systems of linear equations, and principal component analysis.
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Calculus
Multivariable differentiation and integration, series expansions, and function approximation and maximization.
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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, and stochastic independence.
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Statistics/econometrics
Confidence intervals, hypothesis tests, linear regression models, ordinary least squares, likelihood principle, and machine learning.
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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 using Python.
How Can You Assess Your Readiness for the MFin Curriculum?
Use the Financial Mathematics Self-Assessment to evaluate your preparation in the quantitative topics listed above. If you find any areas challenging, consider strengthening them through self-study or formal coursework before enrolling.
Download the MFin Financial Mathematics Self-Assessment (PDF)
How Can You Strengthen Your Quantitative and Programming Skills?
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Programming
DataCamp is an online interactive training and education platform in the field of data science and programming.
Helpful resources to prepare for 15.004 Programming for Finance Professionals are:
• 6.100L: Introduction to CS and Programming using Python (as taught in Fall 2022)
• Python 101 Google Colab Notebook
• Lubanovic (2025) - Introducing Python, 3rd Edition
• McKinney (2022) - Python for Data Analysis, 3rd Edition
• Hilpisch (2018) - Python for Finance, 2nd Edition -
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