AI and Data Mining with Python

Catalog description

This course introduces data mining and introductory AI methods using Python for business decision-making. Students acquire, clean, and transform data; perform exploratory analysis; and build, tune, and compare predictive and descriptive models. Topics include feature engineering; supervised learning (linear and logistic regression, k-NN, decision trees); unsupervised learning (k-means clustering, association rules); model evaluation with train/validate/test splits and cross-validation; and communication of results to non-technical audiences. Work is implemented in Python (e.g., pandas, scikit-learn, matplotlib), with attention to documentation and reproducibility. Ethical considerations include privacy, bias, transparency, and appropriate model use.