IDS/MATH 586

Data Science & Decision Optimization in Banking & Financial Services

This course provides students with all the data science and decision optimization techniques needed for career opportunities in wealth management, consumer and commercial banking and lending, risk management, and stress testing. It teaches students 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.

Case studies include, but are not limited to, modeling and optimization of offer/targeting marketing strategy, modeling and optimization of investment and banking portfolios, modeling and optimization of bank risk and capital, modeling and optimization of funding strategy, and modeling and optimization of banking operations.

In addition to rigorous academic training, students gain practical experience through real-world case studies and class projects with the support of financial industry professionals.

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.