FM Preparation Workshop
A customized three-week program to prepare new students for graduate-level studies in mathematics, statistics and R, econometrics, risk management and Python programming.
Students will have the opportunity to learn directly from instructors in real time on campus, with the option to review recorded lectures after each live session. With over 60 hours of instruction time divided across five distinct learning modules, new students are encouraged to attend the workshop in person. Working professionals may prefer to take advantage of the asynchronous online format. Everyone enrolled in the workshop can expect intensive skill building in financial mathematics core concepts, whether to better prepare for graduate studies or advance in their financial careers.
Registration for 2026 Will Begin Soon!
New, incoming financial mathematics masters students will receive the discounted registration of $2295. The workshop will be held on campus and is scheduled for 9:00AM until 4:15PM each day, including a lunch break.
General registration is $2595 and will require registrants to create a Brickyard account, if they do not have an NC State UnityID. About creating a Brickyard account.
Instructors and Modules
Module 1 – Mathematics
Module 1 Class Objectives
Upon successful completion of this module, students will be able to apply the following tools:
- Measure Theory
- Convergence Theorems
- Riemann and Lebesgue Integrals
- Multi-objective Programming
- Stochastic Control and Derivation of HJB Equations and Analysis
Module 2 – Statistics & R Programming
Module 2 Class Objectives
Course topics include:
- General overview and Introduction to R
- Probability Basics
- Random Variables and Distributions
- Illustrations using R Studio
- Sampling Distributions and Law of Large Numbers
- Parameter Estimation Based on MoM & MLE
- R packages for parameter estimation
- Hypothesis Testing and Confidence Intervals
- Regression Models: Multiple Linear Models
- Illustrations of R for stat inference
Module 3 – Econometrics
Module 3 Class Objectives
Upon successful completion of this module, students will be able to:
- Specify, estimate and then interpret results of a multiple linear regression model using OLS.
- Specify, estimate and then interpret results of a logit or probit model using MLE.
- Use a linear regression model to price options using the Practitioner’s Black-Scholes method.
- Use a logit or probit model to understand how to do credit scoring.
Module 4 – Risk Management
MODULE 4 Class Objectives
The course will be broken down as follows:
- Introduction and Risk Management Overview
- Financial market and market risk
- Asset pricing and quantitative methods
- Financial institutions and credit risk
- Interest rate risk and ALM
- Liquidity risk management
- Operational risk and other risks
- Introduction to risk modeling
- Integrated risk management case studies: Financial Crisis 2008 and the bank failures in 2023
Module 5 – Python & Machine Learning
MODULE 5 Class Objectives
Topics to be covered include:
- Python Basics
- Machine Learning
- Linear Regressions and Multiple Regression
- Logistic Regression and Decision Trees
- Neural Networks and Deep Learning