Experiential Learning

Focuses on independent study under faculty guidance. This is ideal for students wishing to deep-dive into a niche statistical theory, perform literature reviews, or learn a specialized programming package not covered in standard courses.

Note: Credits earned in this course do not count toward Statistics major, minor, or certificate requirements.

Prerequisites: Department permission; typically Statistics/Data Science majors/minors in good academic standing.

Credits: 1–5 Credits (Letter Grade).

Deliverables: Learning proposal, regular progress logs, and a final report/presentation on the mastered topic.

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Focuses on the scientific process. Students contribute to original investigations, data analysis for faculty labs, or the development of new statistical methodologies. This course is for discovery-driven projects rather than independent study.

Prerequisites: Familiarity with coding (Python/R); completion of core methodology (e.g., STA 4210).

Credits: Variable 0–3 Credits (Letter Grade).

Deliverables: Research proposal, regular progress updates, and a manuscript-quality final report or presentation.

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Secure a statistics or data science internship and obtain approval from the departmental coordinator before registration.

Note: Credits earned in this course do not count toward Statistics major requirements.

Prerequisites: STA 4210 and department permission.

Credits: Variable 0–3 (42 verified site hours required per credit).

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Synthesize methods and skills acquired in statistics and data science courses by working on a high-impact project provided by a partnering institution.

Prerequisites: STA 4210 & 3 additional credits in STA 4XXX.

Structure: Team-based research involving a written report, poster, and oral briefing.

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Offered bi-annually (Summer). Explore historical origins of computing. Additional credits available via STA 4930 Overseas Research.