University of Dayton

What you'll study

The programme emphasises the computational and engineering foundations of data science alongside applied statistical methods. Core topics typically include programming for data science (Python/R), probability and statistical inference, machine learning and pattern recognition, data structures and algorithms, relational and NoSQL databases, and data visualisation. Students also study engineering‑oriented subjects such as numerical methods, optimisation, high‑performance and parallel computing, and data engineering for large‑scale systems.

The curriculum is project driven and usually culminates in a capstone or thesis where students work on an applied problem in collaboration with faculty or external partners. Elective options allow deeper study in areas such as natural language processing, deep learning, time‑series analysis, computer vision, cybersecurity for data systems, and domain applications in healthcare, manufacturing or autonomous systems.

Entry requirements

Applicants are expected to hold a bachelor's degree in computer science, engineering, mathematics, statistics, physics or a closely related quantitative discipline. Typical academic preparation includes coursework in programming, calculus and linear algebra, and an introductory course in probability or statistics. Candidates who lack one or more preparatory subjects may be offered conditional admission and required to complete bridge or prerequisite courses.

Application materials generally include official academic transcripts, a statement of purpose describing academic and professional goals, and letters of recommendation. International applicants must demonstrate English proficiency through accepted tests or previously completed academic work in English; additional documentation such as a résumé/CV is commonly requested. Specific programme committees may consider professional experience and prior research when assessing applications.

Career prospects

Graduates enter a broad range of technical roles across sectors. Common career paths include data scientist, machine learning engineer, data engineer, analytics consultant, research scientist and business intelligence developer. The programme’s computational and engineering emphasis also prepares students for roles that require scalable data processing and deployment of models in production, such as systems engineer for AI applications or software engineer with a data focus.

Alumni work in industry, startups, government labs and academic research. The curriculum’s project and capstone components, together with connections to local and regional employers, help students build portfolios and practical experience that employers seek.

Why study at University of Dayton

The University of Dayton provides a collaborative, student‑centred learning environment with strong ties between engineering, computer science and applied mathematics faculty. Small class sizes allow for hands‑on instruction and close mentorship on research and capstone projects. Students benefit from access to departmental computing resources and laboratories that support high‑performance and embedded systems work.

Positioned in a region with manufacturing, aerospace and healthcare employers, the university fosters industry engagement and internship opportunities. Support services such as career guidance, professional development workshops and networking events help students transition from study to professional roles. Additionally, the university’s focus on ethical practice and community engagement encourages graduates to consider the societal impacts of data and AI applications.