Saint Mary’s University of Minnesota

What you'll study

The programme combines foundation courses in statistics and programming with advanced topics in machine learning, database systems and data visualisation. Core subject areas typically include:

• Statistical methods for data analysis: inferential statistics, regression, time series and experimental design.
• Programming and data engineering: Python (or R) for data analysis, SQL and introductory data pipeline concepts.
• Machine learning and predictive modelling: supervised and unsupervised learning, model evaluation and deployment considerations.
• Data visualisation and communication: principles of visual design, dashboarding tools and storytelling with data.
• Big data and cloud concepts: overview of distributed data processing, scalable storage and practical considerations for working with large datasets.
• Ethics, governance and data privacy: responsible use of data, compliance and societal impacts of analytics.
• Capstone project or practicum: an applied project working with real or simulated datasets to solve a domain problem, often developed in partnership with faculty or external organisations.

Courses emphasise hands-on, project-based learning using industry-standard tools and datasets. Students typically complete a series of core modules followed by electives that allow specialisation in areas such as business analytics, health analytics or machine learning.

Entry requirements

Applicants are normally expected to hold an accredited undergraduate degree. Degrees in fields such as computer science, mathematics, statistics, engineering, economics, or other quantitative disciplines are directly relevant. Applicants from other backgrounds with demonstrated quantitative aptitude and some programming experience may also be considered.

Typical prerequisites include familiarity with college-level calculus and statistics, and basic programming skills (for example in Python, R or another high-level language). Where gaps exist, the university may recommend or require preparatory coursework or bridge modules before or during the programme.

Admission decisions are based on academic records, relevant work or project experience, and references. International applicants must satisfy English language proficiency requirements set by the university.

Career prospects

Graduates leave prepared for a range of analytic roles across sectors. Common career paths include:

• Data analyst or business intelligence analyst — using data to support operational and strategic decisions.
• Data scientist or predictive modeller — building and deploying models to forecast outcomes and optimise processes.
• Data engineer or analytics engineer — designing and maintaining data pipelines and storage solutions.
• Analytics consultant or product analyst — translating business questions into analytic approaches and communicating results to stakeholders.
• Specialist roles in sectors such as healthcare analytics, financial analytics, education assessment, public sector data science and non-profit evaluation.

The programme’s practical focus, capstone project and connections to industry help graduates develop a portfolio of work and applied experience attractive to employers.

Why study at Saint Mary’s University of Minnesota

Saint Mary’s offers a learner-centred environment with relatively small cohorts and close faculty mentorship, enabling personalised feedback on projects and coursework. The university emphasises applied learning and ethical formation consistent with its Catholic liberal arts tradition, which can be an advantage for students interested in socially responsible data practice.

The university provides flexible delivery options for many graduate programmes, including face-to-face and online course formats, which suits working professionals. Faculty bringing a mix of academic and industry experience guide students through applied assignments and the capstone experience. Career services, practicum connections and an engaged alumni network support professional development and job placement efforts after graduation.