FIN 580 R1 - Special Topics in Finance - Python: Finance Applications
Lectures and discussions relating to new areas of interest. See class schedule for topics and prerequisites. Course Information: 0 to 4 graduate hours. No professional credit. Approved for Letter and S/U grading. May be repeated to a maximum of 18 hours in a semester; may be repeated to a maximum of 32 hours in subsequent semesters. Credit is not given for FIN 528 and FIN 580 (66393), Section ADF. Prerequisite: Varies by section.
Python: Finance Applications. This course implements a variety of popular data analytics techniques in Python to tackle problems in finance and business. The first part of the course presents backtesting of trading strategies based on simple moving averages, momentum, mean-reversion, and machine learning-based prediction. The second part of the course introduces supervised learning methods for predictive analytics. Methods include Multiple Linear Regression, k-Nearest Neighbors, the Naive Bayes Classifier, Classification and Regression Trees, Logistic Regression, Neural Nets, and Discriminant Analysis. The third part of the course focuses on business time series forecasting. Handling time series, regression-based forecasting, and smoothing-based forecasting will be discussed
Option 1Number of Required Visit(s): 0
Course Level: Graduate