Suggested Background

Basic topics, including: matrix/vector notation, operations on matrices and vectors, determinants, eigenvalues and eigenvectors, systems of linear equations, and principal component analysis.

Multivariable differentiation and integration, series expansions, and function approximation and maximization.

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.

Confidence intervals, hypothesis tests, linear regression models, ordinary least squares, likelihood principle, and machine learning.

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.

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

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