Introduction to Data Science

GE 461 is your first real exposure to the full pipeline of turning raw, messy data into something you can actually defend a conclusion from, collection and storage through preprocessing, modeling, evaluation, and visualization, rather than any single algorithm in depth. The work is entirely project-based: five hands-on assignments where you implement, tune, and validate methods spanning classical parametric models, clustering, classifiers, and a taste of deep and reinforcement learning. Think of it as the breadth course that sits upstream of more focused electives in machine learning, data mining, and computer vision, giving you the vocabulary and pipeline instincts you'll lean on whenever a later course or project hands you a dataset and a question.