Fundamentals of Data Science and Data Analytics

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

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

Beyond core foundations- — including data structures, key model types (OLS, logistic regression, random forests, and neural networks), and supervised versus unsupervised learning paradigms — the course develops essential practical competencies: data preparation, feature engineering, train-test splitting, classification metrics, and ROC analysis. The course also situates neural networks within the broader landscape of modern AI and large language models, while maintaining a clear emphasis on classical data science

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Prerequisites:

Introductory statistics or econometrics is recommended.

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