Quantitative Finance Track

Required Courses

Students must complete 9 credits of required courses.

Electives

Select one Quantitative Elective from COMM, MATH, STAT, ECON, or CS

• COMM 4230 - Fintech: Information Technology in Finance Credits: 3 (If not taken to meet required courses)
• COMM 4259 - Foundations of Machine Learning and AI with Python Credits: 3 - previously COMM 4522
• COMM 4260 - Foundations of Machine Learning and AI with Low-Code Technologies Credits: 3 - previously COMM 4522
• COMM 4730 - Advanced Investments: Derivatives and Fixed Income Credits: 3 (If not taken to meet required courses)
• COMM 4733 - Advanced Investments: Asset Management in the Global Economy Credits: 3 (If not taken to meet required courses)
• COMM 4780 - Institutional Investment Management Credits: 3 (If not taken to meet required courses)
• ECON 4720 - Econometric Methods Credits: 3
• MATH 4110 - Introduction to Stochastic Processes Credits: 3 (Commerce and Math/Statistics double majors only)
• STAT 4120 - Applied Linear Models Credits: 3
• STAT 4630 - Statistical Machine Learning Credits: 3
• STAT 5170 - Applied Time Series Credits: 3

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  Note that admission to the track does not guarantee admission to each of the classes above. Instead, the list of electives is meant to provide a broad set of quantitative courses consistent with the objectives of the track. Typically courses outside of the Commerce School are appropriate for students double majoring in that particular department. Also, other quantitative courses may satisfy the track elective requirement. Please see the Quantitative Finance Track Coordinator about this.

   

  If three of the Quantitative Core are taken, two will satisfy the Quantitative Core requirement and the third will satisfy the elective requirement.