Suggested Background

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

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, covariance, correlation, and stochastic independence.

Parameter estimation, confidence intervals, hypothesis tests, linear regression models, ordinary least squares, and likelihood principle.

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 programming language.

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:

• 6.001: Introduction to Computer Science and Programming in Python (as taught in 2016)

• Ang, Analyzing Financial Data and Implementing Financial Models Using R

• Ruppert and Matteson, Statistics and Data Analysis for Financial Engineering

• Arratia, Computational Finance

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