BAN485 Data Mining II: R, Python, and Applied AI

This course extends data mining with hands-on work in R and Python and selective use of AI. Students prepare and visualize data, build and compare models, and communicate results for business decisions. Topics include linear and nonlinear regression, logistic regression, association-rule mining (market basket analysis), and k-means clustering. Projects include analyzing social-media and survey datasets with Python and AI to extract signals, generate first-pass summaries, and validate findings. Emphasis is on feature engineering, evaluation and validation, interpretability, and clear recommendations.

Prerequisites: BAN385