46-921 Financial Data Science I

6 units, Carnegie Mellon University

The first in a two-course sequence covering methods of extracting useful information from raw financial data. Focus is placed on fundamental tools of statistical inference useful for fitting models to financial data, including those with complex multivariate forms. Topics include linear models, nonparametric models, parameter estimation, and uncertainty quantification. Methods are taught using the Python programming language. Non-MSCF students may not take this course without written permission from the instructor. To be eligible, you must be a BSCF student or a graduate student enrolled in an MSCF participating college/department (Stats & Data Science, Tepper, Computer Science Dept., or Math Sciences). PhD students with relevant research may be eligible with permission from the instructor.

Prerequisites: none listed

Offered: Fall 2026, Fall 2025

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