Who Should Take This Course and Why
For those who lack the math background or simply want a refresher, the Financial Mathematics Program offers a Preparation Course that covers mathematical concepts required for coursework in the Master of Science in Financial Mathematics (MSFM) program, and required of applicants to the program. More generally, the Preparation Course is targeted at students interested in taking a Master level course in quantitative finance or financial engineering. Finance and non-finance industry professionals who would like a refresher course in mathematics are also welcome to register for this course.
The Preparation Course focuses more on mathematics, rather than strictly on finance, in order to build a solid foundation in Calculus, Probability, Linear Algebra, and Python, to allow students to enhance their financial mathematics skills with further studies in the field. This Preparation Course is designed to be an intense and accelerated introduction to concepts in mathematics that are essential for future careers in quantitative finance.
A key aim of the course is to impart to students the technical skills for them to be able to deal with graduate level financial mathematics and problem solving. Lectures will also include Financial and Economic applications. Past participants in the Preparation Course have included:
• Students with economics, business, or finance backgrounds looking to learn and expand their knowledge of mathematics in areas they may not have previously covered.
• Students with science or engineering backgrounds looking to solidify and sharpen their mathematics skills.
The Preparation Course is open to the public. If a Preparation Course student applies for the MSFM program, the student's participation in the Preparation Course (and in any other coursework providing math background for the MSFM program) will be taken into account, in making the MSFM admissions decision (with no guarantee of admission).
Lectures cover topics in Calculus, Probability, Linear Algebra, and Python.
Spanning two modules, the Preparation Course is summarized as follows:Module 1: Calculus and Probability
Calculus: Limits and series, differential and integral calculus in one and multiple dimensions, implicit differentiation, integration by parts, Taylor’s Theorem, convexity and Jensen's inequality, L'Hospital's rule, application of calculus in mathematical finance.
Probability: Combinatorics, discrete and continuous probability distributions, density functions and cumulative distribution function, conditional probability and conditional expectation, central limit theorem, moment-generating functions, strong law of large numbers, application of probability theory in solving quant interview questions.Module 2: Linear Algebra and Python
Linear Algebra: Vectors and matrices, systems of linear equations, fundamental theorem of linear algebra, vector spaces and subspaces, eigenvalues and eigenvectors, spectral decomposition, diagonalization, orthogonality and orthogonalization, projection and linear regression, determinants, positive-definite matrices and covariance matrices, SVD and Principal Component Analysis, application of linear algebra in finance.
Python: Fundamentals of Python programming, data visualization using Matplotlib, numerical computation using Numpy, Scipy, and Sympy, examples illustrating calculus, linera algebra and probability concepts, and assignments covering Monte Carlo simulation and variance analysis, root finding, numerical integration, implied volatility, and Markov chain.
This course is not taken for credit nor are students enrolled through the University's Registrar system. Therefore, an official University of Chicago transcript is not available. There are no exams and official course grades are not given.
Homework is assigned and graded, measuring progress throughout the duration of the course. Further details about homework will be provided by the instructor.
Students who need a grade, or a letter certifying completion of the Preparation Course, can request a letter from the instructor. It is the responsibility of each individual student to request and follow up when a grade or letter is required. This option is available only to students who make a legitimate effort to complete course homework.
The Financial Mathematics Preparation Course does not lead to a degree. Successful completion of the course does not guarantee admission to the Master's program in Financial Mathematics.
Classes are conducted on Monday and Thursday evenings from 7:45pm to 9:00pm (Chicago time).
Spring 2017 Prep Course
Module 1: May 4, 2017 to June 26, 2017
Module 2: June 29, 2017 to August 24, 2017
Fall 2017 Prep Course
Module 1: September 25, 2017 to November 16, 2017
Module 2: January 4, 2018 to March 5, 2018
Classes are conducted via live online meeting and are recorded and made available for later viewing.
The cost of the Preparation Course is US$2,200 per module, with the following discounted rates:
Early registration and payment on or before September 20, 2017: US$2,000
Regular registration and payment after September 20, 2017: US$2,200
Early registration and payment on or before Decembre 29, 2017: US$2,000
Regular registration and payment after December 29, 2017: US$2,2000
Group discount: if two or more students from the same company or school are registering together, then each student in the group is entitled to deduct 25% from the listed tuition rates.
There will be no refunds issued for withdrawal.
Email: Nina Bachkova at email@example.com
Tel: +1 773 834 0785
Chao-Jen Chen is a senior instructor in the MSFM program’s Preparation Course and a senior teaching assistant in the MSFM program, mainly responsible for running review classes for courses including Option Pricing, Numerical Methods, and Machine Learning in Python. He used to work for one of the largest insurers in Singapore as fixed income quantitative analyst and FX manager, managing and hedging the insurer’s USD3bn FX exposure to more than 20 currencies. His recent research interest is Singapore’s monetary policy tool, i.e., the SGD NEER (Singapore Dollar Nominal Effective Exchange Rate). In April 2017, he released an app in Google Play and Apple App Store that implemented his SGD NEER model.
Chao-Jen holds a bachelor’s degree in Mathematics from National Tsing Hua University and master’s degrees in Industrial Engineering from National Taiwan University and Financial Mathematics from the University of Chicago.