Saint Mary’s University of Minnesota

What you'll study

The programme blends foundational coursework in mathematics and statistics with applied computer science and domain-oriented analytics. Core topics typically include:

• Introduction to programming and data structures (commonly using Python and SQL)
• Statistical methods for data analysis, probability and inference
• Database design and management, including relational databases and querying
• Data wrangling, cleaning and ETL (extract, transform, load) techniques
• Data visualization and dashboarding to communicate results to non-technical audiences
• Introductory machine learning and predictive modelling (supervised and unsupervised methods)
• Big data concepts and working with larger-scale or cloud-based tools
• Ethics, privacy, and governance of data
• Capstone project or practicum where students apply methods to a real dataset, often in partnership with an external organisation

Typical degree structure includes general education requirements alongside major courses, elective options to specialise (for example in business analytics, healthcare analytics or geographic information systems), and opportunities for internships and research with faculty. Labs and experiential assignments emphasise reproducible workflows, version control and teamwork.

Entry requirements

Applicants should hold an accredited secondary-school diploma or equivalent. Successful candidates typically demonstrate competence in mathematics—courses such as algebra and precalculus are useful foundations—and any prior exposure to computer science or statistics is advantageous. Admissions decisions are based on academic records and supporting documents; applicants may be asked for transcripts, a personal statement and references. Transfer students with relevant college-level coursework or articulation agreements are considered, and work experience in IT or quantitative roles can strengthen an application. Standardised test requirements vary by campus and may be considered optional in some circumstances.

Career prospects

Graduates enter a wide range of sectors where organisations use data to inform decisions. Common entry-level and mid-level roles include:

• Data analyst or business intelligence analyst
• Reporting analyst or operations analyst
• Marketing or customer insights analyst
• Healthcare data analyst or public health data specialist
• Database developer or junior data engineer

With practical experience and further study or certifications, graduates may progress to roles such as data scientist, machine learning engineer, analytics manager or consultant. The programme’s emphasis on applied projects and internships helps students build portfolios and professional networks valuable to employers in finance, healthcare, manufacturing, government, education and nonprofit organisations.

Why study at Saint Mary’s University of Minnesota

Saint Mary’s offers a smaller-campus environment with a liberal arts foundation that emphasises hands-on learning and close faculty mentoring. The university integrates classroom learning with practical experience through lab work, capstones and local internship partnerships, helping students develop a professional portfolio. Faculty bring a mix of academic and applied experience, and career services support resume development, interview preparation and employer connections. Students benefit from a values-informed community, opportunities for cross-disciplinary study, and flexible delivery options that can include in-person and hybrid coursework to accommodate different learning and working needs.