St. Thomas University (FL)

What you'll study

This programme builds a core of mathematical theory alongside hands-on data science practice. Early semesters emphasise calculus, linear algebra, discrete mathematics and introduction to programming (typically Python), while intermediate modules introduce probability theory, mathematical statistics, numerical analysis and data structures. Core data science topics include statistical inference, regression and predictive modelling, machine learning, data visualisation, databases and applied optimisation.

Students gain practical experience through laboratory courses and project work: applied computational mathematics, time series analysis, and capstone projects that involve end-to-end data workflows (data acquisition, cleaning, analysis, modelling and communication). Electives allow specialisation in areas such as big data technologies, scientific computing, financial mathematics, actuarial methods or bioinformatics. Coursework emphasises reproducible computing, ethical use of data and clear presentation of quantitative results.

• Foundations: Calculus I–III, Linear Algebra, Discrete Mathematics
• Core statistics & probability: Probability Theory, Mathematical Statistics, Applied Regression
• Computation & data: Programming for Data Science (Python/R), Data Structures & Algorithms, Databases
• Applied mathematics: Numerical Analysis, Optimisation, Mathematical Modelling
• Data science specialisms: Machine Learning, Time Series & Forecasting, Data Visualisation, Big Data Methods (elective)
• Capstone: Senior Project or Internship with an applied research or industry partner

Entry requirements

Typical admission requires a high school diploma or equivalent with strong performance in mathematics. Successful applicants usually present coursework in algebra, geometry, pre-calculus or calculus; prior programming experience is helpful but not always mandatory. Admissions will consider overall academic record, personal statement and any relevant extracurricular experience or portfolio work.

For transfer applicants, evidence of college-level calculus or statistics is often expected. International applicants must demonstrate English proficiency in line with the university's general requirements. Competitive applicants may also be invited to an interview or asked for letters of recommendation.

Career prospects

Graduates are prepared for a broad set of quantitative and data-focused roles across industries. Common entry-level job titles include data analyst, junior data scientist, business intelligence analyst, quantitative analyst, and analytics consultant. The mathematical emphasis also supports careers in modelling and simulation, operations research, actuarial work (with further professional study), and research roles in science and engineering.

Many graduates progress to specialised roles in finance, healthcare analytics, technology firms, government and consulting, or pursue graduate study in data science, applied mathematics, statistics or computer science. The programme’s project and internship components help build a professional portfolio and connections with employers in the region.

Why study at St. Thomas University (FL)

St. Thomas University offers a personalised learning environment with small class sizes and close faculty mentorship, which benefits students mastering both abstract mathematics and practical coding skills. The university’s location in the Miami area provides access to a diverse economy with opportunities in healthcare, finance, technology and government for internships and applied projects.

The curriculum reflects an integrated liberal-arts perspective, emphasising critical thinking, ethical use of data and clear communication—skills valued by employers. Students can take advantage of computing labs, collaborative project spaces and faculty-led research, and the programme’s capstone and internship options are designed to connect academic learning with real-world experience.