DS 430: Business Forecasting with Python

Course Description

This course explores a diverse range of quantitative modeling methods crucial for forecasting. It includes topics such as moving averages, various smoothing techniques, trend and seasonal forecasting, decomposition approaches, and both univariate and multivariate regression methods. Additionally, it explores the autoregressive integrated moving average (ARIMA) approach. Judgmental forecasting techniques are also covered. Through hands-on laboratory exercises and an applied forecasting project, students actively engage with selected software packages, integrating Python programming for real-world applications (F, W).