The purpose of this course is to introduce students to various applications of data science and quantitative techniques in the banking and financial industry. Through hands-on case studies, students will be exposed to real-world projects, the most pressing problems and the latest innovations in the field of quantitative finance and understand how machine learning/data science techniques and other computational skills are combined with optimization methods to solve real-word finance problems. Students will experience the whole process of solving quantitative problems in banking and finance from start to finish, i.e., from problem formulation, data structuring, predictive learning and estimation, strategy and decision optimization to solution evaluation, proposal communication and results monitoring.
Because quantitative finance is so broad, topics often change each semester from capital markets to banking to insurance.
For example, in the fall 2023 semester, this course focused on retail and commercial banking and lending. Using consumer lending such as credit cards, students learned how to build statistical models and develop optimal decisions in the banking and financial services industries using machine learning and artificial intelligence techniques, traditional statistical modeling skills, decision optimization methods, and economic and financial theory.
Prerequisites:
- College-level calculus, Linear Algebra (e.g., MATH 216, 218 0r 211), probability and statistics (e.g., MATH/STA 230, MATH 340/STA 231 or Math 238L/EGR 238L).
- Basic programming skills in Python, R or SAS.
- While prior knowledge in finance is not required, some basic understanding of economics, finance, and financial institution is preferred. Intellectual curiosity and independent study habits are strongly desired.
Hands-on class projects will account for the major part of a student’s final grade.