The MSMF is a STEM-designated program with a highly interdisciplinary curriculum. Our courses are taught by an outstanding team of faculty members and expert industry practitioners. All courses are 3 credits unless stated otherwise and not all courses are offered every semester.
MSMF Degree
The MSMF degree consists of six core courses, one master essay course, three approved MSMF electives, and two semesters of seminars. The MSMF degree can be pursued alone without following the concentrations below.
Concentrations
The program currently offers two concentrations; Risk Management and Data Science. The concentration consists of the MSMF degree and a certificate in that area.
1. Data Science: The analysis of large data sets has become an important task for professionals in many industries. Employers of our students increasingly request evidence of expertise in data analysis, in addition to those in mathematical finance. In response to this demand, we seek to provide MSMF students with a concentration that can be tailored to the specific needs in the area, as well as enhance their career opportunities. The list of courses will be updated regularly to reflect the industry trend as well as changes in the field of Data Science.
Data Science Track ?
The Data Science concentration requires six core courses, ECE 503, one master essay course, two approved MSMF electives, two semesters of seminars and three Data Science suggested electives (see table below):
Data Science Suggested Electives
Stat | 16:960:588 | Data Mining |
Stat | 16:954:577 | Advanced Analytics Using Statistical Software |
CS | 16:198:512 | Introduction to Data Structures and Algorithms |
DS | 16:954:597 | Data Wrangling |
CS | 16:198:539 | Database Management Systems |
2. Risk Management: The knowledge of risk management is essential for a professional working in the financial industry. There is always a good demand for graduates with expertise in this area. In response to this demand, we seek to provide MSMF students with a concentration that can be tailored to their specific needs and will enhance their career opportunities. The list of courses will be updated regularly to reflect the industry trend as well as changes in the field of Risk Management.
Risk Management Track ?
The Risk Management concentration requires six core courses, ECE 503, one master essay course, two approved MSMF electives, two semesters of seminars and three Risk Management suggested electives (see table below):
Risk Management Suggested Electives
FSRM | 16:958:587 | Advanced Simulation Methods for Finance |
FSRM | 16:958:590 | Foundations of Financial Statistics and Risk Management |
FSRM | 16:958:535 | Advanced Statistical Methods in Finance |
FSRM | 16:958:534 | Advanced Statistics for Risk Management |
Core Curriculum
The program requires the completion of ten (10) three-credit courses (6 required) and two semesters of the seminar course programs.
Core Courses ?
Math | 16:643:621 | Mathematical Finance I |
Math | 16:643:622 | Mathematical Finance II |
DS | 16:958:563 | Regression Analysis In Finance |
FSRM | 16:958:565 | Financial Time Series Analysis |
Math | 16:643:573 | Numerical Analysis I |
Math | 16:643:623 |
Computational Finance |
Math | 16:643:630 | Seminar in Mathematical Finance (0 Credits) |
Core Substitution Courses (Department Approval Required) ?
Econ | 16:220:506 | Advanced Economics Statistics |
ISE | 16:540:530 | Forecasting and Time Series Analysis |
Stat | 16:960:563 | Regression Analysis |
Stat | 16:960:565 | Applied Time Series Analysis |
Stat | 16:960:586 | Interpretation of Data I |
Approved MSMF Electives
Students may select from a variety of approved electives from the Department of Mathematics, Statistics, Computer Science, ECE, Business. Please consult with our Senior Program Coordinator Supervisor regarding electives offered in other departments and schools before registration.
Mathematics Finance Electives (Strongly Recommended) ?
ECE | 16:332:503 | Programming Methodology for Finance (C++) |
Math | 16:643:574 | Numerical Analysis II |
Math | 16:643:628 |
Topics in Mathematical Finance: Machine Learning In Quantitative Finance - Fall 2022 Topics in Mathematical finance: Analysis of Large Scale Industry Events in Quantitative Finance - Fall 2023 |
Math | 16:643:626 | Fixed-income Securities and Derivative Modeling (master essay included) |
Math | 16:643:625 | Portfolio Theory and Applications (master essay included) |
Math | 16:643:631 | Mathematical Methods for Financial Risk Management (master essay included) |
Statistics Electives ?
Stat | 16:958:535 | Advanced Statistical Methods in Finance | |
Stat | 16:960:540 | Statistical Quality Control | |
Stat | 16:960:565 | Applied Time Series Analysis | |
Stat | 16:960:583 | Methods of Statistical Inference | * No credit with Stat 567 or Econ 506 |
Stat | 16:960:587 | Interpretation Data II | |
Stat | 16:960:567 | Applied Multivariate Analysis | * No credit with Stat 583 |
Stat | 16:960:588 | Data Mining |
Electrical and Computer Engineering Electives ?
ECE | 16:332:566 | Introduction to Parallel and Distributed Programming | |
ECE | 16:332:567 | Software Engineering I | |
ECE | 16:332:569 | Database System Engineering | * No credit with CS 541 |
Computer Science Electives ?
CS | 16:198:536 | Machine Learning | |
CS | 16:198:541 | Database Systems | * No credit with ECE 569 |
CS | 16:198:520 | Introduction To Artificial Intelligence | |
CS | 16:198:512 | Introduction to Data Structures and Algorithms | |
CS | 16:137:562 | Applied AI Concept to Market | |
CS | 22:198:603 | Business Data Management | |
CS | 26:198:622 | Machine Learning | |
CS | 22:198:660 | Business Analytics Programming | |
CS | 26:198:685 | Introduction to Algorithms and Data Structure |
Business Electives ?
Bus | 22:390:601 | Risk and Insurance Management | |
Bus | 22:390:603 | Investment Analysis and Management | |
Bus | 22:390:608 | Portfolio Management | * No credit with Math 625 |
Bus | 22:390:611 | Analysis of Fixed Income Securities | * No credit with Math 626 |
Bus | 22:430:603 | Investment Analysis and Management | |
Bus | 16:137:539 | Introduction to Cloud and Big Data Systems | |
Bus | 16:137:552 | Python Methodologies |