Honors AI and Data Applications in Finance

(3 Credit Hours) · Undergraduate

With increased data availability and the rapid advancement of artificial intelligence, finance professionals need both strong data analysis skills and the ability to leverage modern AI tools effectively. This course introduces students to AI and data applications in finance using the Python programming language and its ecosystem of packages. The course is structured in four main parts. First, you will build a solid foundation in Python programming for finance, learning to work with NumPy and Pandas to collect, analyze, and visualize financial and macroeconomic data. Second, you will be introduced to AI-assisted coding tools (such as Claude, ChatGPT and similar AI coding assistants) that help you write better code, understand complex codebases, and accelerate your workflow. You'll learn how to use AI tools to make your work more efficient and rigorous. Third, you will learn machine learning techniques for finance, including dimensionality reduction with Principal Component Analysis (PCA) and penalized regression methods (LASSO, Ridge) to make predictions and identify patterns in financial data while avoiding common pitfalls like overfitting. Fourth, you will gain exposure to large language models (LLMs) and natural language processing, learning how to apply these tools to analyze financial text, classify sentiment, and build retrieval-augmented generation (RAG) systems for finance applications. Throughout the course, you will work on practical finance applications: characterizing security price movements, evaluating portfolios, predicting stock returns, and analyzing financial texts. As the field of artificial intelligence continues to grow in finance, possessing an understanding of how to leverage AI tools effectively becomes increasingly valuable for finance professionals. This course provides a practical introduction to these tools within a finance context. In the honors version of the course, students will engage more deeply with the research frontier of AI, machine learning, and text analysis in finance, including a small group project replicating the core analysis of a published finance and machine learning paper using modern computational methods in Python.