FIN4975 Data Analysis in Finance

This course uses statistical programming tools to analyze financial data. We use the Python programming language and important libraries, such as NumPy and pandas. Topics include data cleaning, plotting, time series, portfolio optimization, linear regression, simulation, and risk management. Machine learning techniques are also introduced. No prior coding experience is expected.

Credits

4 sh

Prerequisite

A grade of C+ or better in FIN 3430.

Offered

• Spring

Course Outcomes

1. Gain basic proficiency in the Python programming language and other software tools, such as version control, data APIs, AI-based coding assistants, and the VS Code environment.
2. Become familiar with common data analysis tasks related to finance, such as data cleaning/preparation, using real financial data sets (e.g. returns, firm-level accounting data, and real estate).
3. Become familiar with common statistical techniques used in finance, such as linear regression and optimization.
4. Become familiar with basic machine learning methods (e.g. supervised vs. unsupervised learning) and compare these to traditional tools.
5. Complete a student-lead project from start to finish, learning how to use financial theory to ask and answer questions of data.