CHEME 5660 - Financial Data, Markets, and Mayhem for Scientists and Engineers

Fall. 3 credits. Student option grading.

Prerequisite: Knowledge of programming languages, such as Python, Matlab, Julia and mathematical and computing topics, such as probability, statistics, optimization, and data science tools, such as Jupyter notebooks, DataFrames, etc., is helpful.

J.D. Varner, A. Woltornist.

A quantitative finance course that enables scientists and engineers to make quantitative financial decisions in corporate and wealth management contexts. We’ll use tools from engineering, statistics, artificial intelligence (AI), data science (DS), and machine learning (ML) to model, analyze, and ultimately optimize financial systems and financial decision-making. The material from this course can be applied to traditional economic and engineering fields while simultaneously providing a core set of tools for students interested in entrepreneurship or opportunities in the financial and consulting industries. Course assignments will be completed using LaTeX.

Outcome 1: Analyze financial data sets using tools from artificial intelligence (AI), data science (DS), and machine learning (ML).

Outcome 2: Identify quantitative models of asset pricing and process performance using real-time and static financial data sets.

Outcome 3: Demonstrate mastery of quantitative decision-making and risk management approaches in the context of corporate finance and personal wealth management.