St. Thomas University (FL)

What you'll study

The programme combines core training in statistics, programming and machine learning with practical coursework in data engineering, visualisation and applied analytics. Typical modules include:

• Statistical Methods for Data Science – probability, inference, regression and model evaluation.
• Machine Learning – supervised and unsupervised algorithms, model selection and regularisation.
• Data Engineering and Big Data – data modelling, ETL pipelines, databases, and working with distributed systems.
• Data Visualisation and Communication – principles of visual analytics, dashboarding and storytelling with data.
• Programming for Data Science – applied Python/R, software engineering practices, reproducible research and version control.
• Ethics, Policy and Social Impact of Data – privacy, fairness, governance and responsible use of algorithms.
• Electives and Domain Applications – students can typically choose modules that focus on finance, healthcare, business analytics, or natural language processing.
• Capstone Project or Internship – a substantial applied project with an industry partner or research-led dissertation that demonstrates end-to-end data work from problem formulation through deployment and evaluation.

Teaching methods emphasise hands-on labs, project-based assessments and opportunities to work with local employers in the Greater Miami area. The curriculum is intended to balance theoretical foundations with immediately applicable skills.

Entry requirements

Applicants are normally expected to hold a bachelor’s degree from an accredited institution. Competitive applicants typically have a degree in a quantitative discipline (for example mathematics, statistics, computer science, engineering, economics) or equivalent professional experience demonstrating quantitative and programming competence.

• Official transcripts from prior post-secondary study.
• A résumé/CV outlining relevant academic and professional experience.
• A personal statement describing motivation for graduate study and relevant experience in programming, statistics or analytics.
• Letters of recommendation (often one to three) from academic or professional referees.
• Some programmes may request evidence of programming ability (code samples, coursework) or require completion of preparatory courses if applicants lack a quantitative background.
• International applicants will need to demonstrate English language proficiency according to the university’s published requirements.

The programme may consider applicants with strong professional experience even if their undergraduate degree is not strictly quantitative, but such applicants should expect to complete prerequisite modules to ensure readiness for core data science coursework.

Career prospects

Graduates go on to a range of technical and applied roles across sectors. Common career pathways include:

• Data Scientist — building predictive models, designing experiments and translating models into business impact.
• Data Analyst / Business Intelligence Analyst — extracting insights, building dashboards and supporting decision-making.
• Machine Learning Engineer — productionising models, building data pipelines and integrating ML into applications.
• Data Engineer — designing and operating scalable data architectures and ETL processes.
• Analytics Consultant or Domain Specialist — applying data methods to finance, healthcare, marketing or public policy problems.

Students benefit from the university’s connections in the South Florida region for internships and employer projects, and the capstone provides a practical portfolio piece to show prospective employers.

Why study at St. Thomas University (FL)

St. Thomas University offers a personalised, student-centred environment with smaller class sizes that allow for close faculty interaction and mentoring. The university emphasises ethical reflection and social responsibility as part of technical training, providing students with a framework to consider privacy, fairness and community impact when building data-driven solutions.

Located in the Miami metropolitan area, the university provides access to a diverse set of industry partners across finance, healthcare, technology and public sector organisations, which supports applied projects and internship opportunities. The programme’s focus on practical skills, a capstone experience and career support is geared to help graduates transition into data roles or advance within their organisations.