Fundamentals of Machine Learning and Data Science

This course offers a practical and accessible introduction to data science for upperlevel undergraduate and graduate students seeking to build genuine analytical skills in astructured, applied setting. Students are guided through the full data science workflow —from understanding and cleaning data, to building predictive models and evaluating theirperformance with rigor and responsibility.

Bridging programming, statistics, and problem-solving in an AI-driven world, the courseemphasizes the data tasks most relevant to applied research, business analytics, andintroductory machine learning. Students work extensively with Python-based materialsand real-world examples throughout the semester, leveraging modern AI tools as anintegral part of the programming workflow.

Beyond core foundations- — including data structures, key model types (OLS, logisticregression, random forests, and neural networks), and supervised versus unsupervisedlearning paradigms — the course develops essential practical competencies: datapreparation, feature engineering, train-test splitting, classification metrics, and ROCanalysis. The course also situates neural networks within the broader landscape of modernAI and large language models, while maintaining a clear emphasis on classical data sciencemethods for structured, tabular data.

Prerequisites:

Introductory statistics or econometrics is recommended.

Prior programming experience is helpful but not required; the course materials are designed tosupport beginners in Python.