University of Dayton

What you'll study

The programme combines core coursework in statistical inference, machine learning, database systems and data management with applied modules in data visualisation, predictive modelling and big data technologies. Students typically study topics such as:

• Statistical methods and applied probability — regression, hypothesis testing and experimental design for data-driven decision making.
• Machine learning and predictive analytics — supervised and unsupervised learning, model evaluation and feature engineering.
• Data management and databases — SQL, data warehousing, ETL processes and fundamentals of NoSQL systems.
• Programming for data science — practical skills in languages and tools commonly used in industry, such as Python, R and relevant libraries.
• Data visualisation and communication — principles and tools to present analytic results to technical and non-technical audiences.
• Big data and cloud technologies — concepts and hands-on exposure to distributed processing frameworks and cloud-hosted analytics.

Program structure usually includes a combination of core modules, elective options that allow specialisation (for example in business analytics, engineering analytics or healthcare analytics), and a substantial applied component. Students complete either a capstone project with an industry partner or a research-oriented thesis, giving practical experience in solving real-world data problems.

Entry requirements

Applicants are expected to hold a recognised bachelor's degree. Degrees in mathematics, statistics, computer science, engineering, business or related quantitative disciplines are typically preferred. The admissions committee looks for a solid foundation in quantitative reasoning and some programming exposure.

• Academic transcripts from previous tertiary study.
• A personal statement outlining academic and professional goals, and reasons for choosing the programme.
• Curriculum vitae or résumé detailing relevant experience.
• Letters of recommendation from academic or professional referees may be requested.
• International applicants must meet English language proficiency requirements (e.g. approved tests such as TOEFL or IELTS) if their prior instruction was not in English.

Standardised test requirements (such as the GRE) and the exact prerequisites vary by applicant background; prospective students with limited quantitative preparation may be advised to complete preparatory coursework prior to or during the programme.

Career prospects

Graduates enter a broad range of roles that apply data-driven decision making. Typical job titles include data analyst, data scientist, business intelligence analyst, machine learning engineer and analytics consultant. Because the programme emphasises applied skills and collaboration with local industry, alumni find opportunities in sectors such as finance, healthcare, manufacturing, aerospace, retail and public sector organisations.

The University of Dayton’s regional employer networks, internship placements and organised career services support students in securing practicum placements and full-time roles. Graduates may also continue into research or PhD programmes if they are interested in academic or advanced technical careers.

Why study at University of Dayton

The University of Dayton offers a student-centred learning environment with relatively small class sizes and strong emphasis on experiential education. The university links technical training with ethical reasoning and communication skills, preparing graduates to apply analytics responsibly in organisational settings.

Students benefit from access to multidisciplinary faculty, laboratory facilities and partnerships with local and regional employers, which support applied projects and internship opportunities. The campus career services and alumni network further assist in professional development and job placement.

Overall, the programme is suited to those who want a practical, hands-on master’s education in data analytics grounded in both technical competence and real-world application.