Applied Data Science & Machine Learning Methods with R & Python

Catalog description

This course provides a framework for hands-on development of end-to-end data science workflows in R and Python. Topics include exploratory analysis, data preparation, web data acquisition, supervised learning (classification; regression & time-series forecasting), unsupervised learning (clustering, PCA, association analysis), and a domain application integrating generative AI. There is an emphasis on model evaluation, reproducibility, and ethical use of data and AI. Registration Restrictions: Junior or Senior Status