Utah Valley University

What you'll study

Core learning areas include probability theory, statistical inference, regression and generalized linear models, experimental design, time series and multivariate methods, and applied data analysis using statistical programming. Electives typically allow deeper study in areas such as computational statistics, data visualization, Bayesian methods, or domain-specific applications where statistics is used to drive decisions.

Entry requirements

Admission normally requires completion of secondary education or an associate-level credential and readiness in college-level mathematics. Incoming students should be comfortable with algebra and prepared to study calculus and linear algebra early in the programme. Transfer applicants usually need to demonstrate completion of prerequisite mathematics courses; specific course-equivalency and GPA expectations are set by the university.

Career prospects

Graduates are prepared for roles that require quantitative reasoning and data analysis, such as data analyst, statistical consultant, quality analyst, research assistant, or positions in government, healthcare, finance, marketing, and technology. Many alumni also continue to graduate study in statistics, biostatistics, data science, or related fields when they seek more specialised or research-focused careers.

Scholarships & funding angle (general)

Students should explore institutional scholarships for undergraduate STEM majors, merit-based awards, and department-level support; many universities also list assistantships, research internships, and work-study opportunities that provide both funding and practical experience. External scholarships from professional statistical societies and industry partners can help defray costs and build professional networks.