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Description

The proposed two-semester sequence is designed to provide students with a rigorous foundation in statistical thinking, computational modeling, and modern machine learning. The curriculum integrates probability, inference, optimization, and algorithmic learning into a coherent framework for data-driven decision-making. The sequence progresses from statistical foundations to computational machine learning systems, emphasizing both theory and implementation.

Department of Primary Author

Mathematics

Affiliation of Primary Author

Faculty

Publication Date

2026

Designing a Two-Semester Undergraduate Data Science Sequence for Computer Science, Mathematics, and Actuarial Science Majors

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