Boise State University

What you'll study

The programme combines foundational coursework in mathematics and statistics with practical training in programming, databases, and data visualisation. Early coursework builds quantitative skills (calculus, linear algebra, probability and inferential statistics) and introduces programming languages commonly used in analytics such as Python and R.

• Programming and software: Python, R, version control, scripting for data processing.
• Data management: Relational databases, SQL, data modelling, data warehousing concepts.
• Statistical methods: Regression, hypothesis testing, experimental design and applied statistical inference.
• Data analytics and machine learning: Supervised and unsupervised learning methods, predictive modelling and evaluation.
• Data visualisation and communication: Principles of visualisation, dashboarding tools and communicating results to stakeholders.
• Big data and cloud technologies: Introduction to distributed processing, working with large-scale datasets and cloud-based analytics platforms.
• Ethics and policy: Data ethics, privacy, legal considerations and responsible use of analytics.

The degree typically culminates in a capstone project or practicum where students apply methods to a substantive real-world problem, often partnering with industry, government or campus research projects. Elective options and cross-disciplinary courses let students focus on application domains such as business analytics, health data, geospatial analysis or social data.

Entry requirements

Applicants should hold a high-school diploma or equivalent. Successful applicants typically demonstrate strength in mathematics (algebra and precalculus at minimum; prior exposure to calculus is advantageous) and some experience or aptitude in computing. Admissions decisions are based on a combination of academic record, coursework, and any required institutional application materials.

International applicants need proof of secondary-school completion equivalent to U.S. qualifications and must satisfy the university’s English language requirements (for example via recognised tests or previously completed instruction in English). Transfer applicants are assessed on college-level coursework; relevant community-college credits in mathematics, statistics or programming can be advantageous.

Career prospects

Graduates are prepared for entry-level professional roles that require turning data into insight and recommendations. Typical job titles include data analyst, business intelligence analyst, reporting analyst, operations analyst and junior data scientist. With additional specialisation or experience, graduates also move into roles such as data engineer, machine learning engineer, analytics consultant or product analyst.

Graduates find opportunities across sectors—technology, finance, healthcare, manufacturing, retail, government and non-profit organisations—wherever organisations rely on data to guide strategy, operations and customer engagement. The capstone and internship components are designed to help students build a portfolio of applied work and industry contacts that support job entry and career progression.

Why study at Boise State University

Boise State offers a practical, hands-on approach to analytics education with access to faculty from mathematics, computer science and applied fields. Students benefit from experiential learning through laboratory courses, project-based capstones and local industry partnerships in the Boise metropolitan area.

The university’s campus environment emphasises applied research and collaboration across disciplines, providing opportunities to work with faculty-led research groups and university centres that engage on real problems. Smaller class sizes in upper-level courses allow close mentoring, and the programme’s connections with regional employers support internships and career placement. For students who want a blend of strong quantitative training and practical workplace experience, Boise State provides a pathway into the rapidly growing data analytics workforce.