Florida Polytechnic University

What you'll study

This master's focuses on the foundations and practical application of computational and data science. Core themes include statistical modelling and inference, supervised and unsupervised machine learning, data engineering for large-scale data, interactive visualisation, and the use of high-performance and cloud computing for data-intensive tasks.

• Core modules: probability and statistical inference; machine learning and pattern recognition; databases and data management; algorithms for data science; data visualisation and communication.
• Advanced and elective modules: deep learning; natural language processing; time-series analysis and forecasting; big data systems and distributed computing (Hadoop/Spark paradigms); optimisation and numerical methods; applied Bayesian methods.
• Computational infrastructure: courses and labs include work on parallel computing, GPU-accelerated model training, and deployment on cloud platforms to ensure students can scale analyses from prototype to production.
• Capstone / thesis: students complete either an industry-oriented practicum/capstone project or a research thesis under faculty supervision. Projects typically partner with industry or focus on interdisciplinary problems in areas such as smart cities, healthcare analytics, manufacturing, or autonomous systems.
• Skills developed: programming in Python/R, SQL and NoSQL data stores, model evaluation and validation, reproducible research practices, data pipeline design, and communicating technical results to non-technical stakeholders.

Entry requirements

Applicants are expected to hold an undergraduate degree in computer science, engineering, mathematics, statistics, physics or a closely related quantitative field. Relevant professional experience in programming or data analysis can strengthen an application for candidates from other backgrounds.

• Transcripts demonstrating solid quantitative coursework such as calculus, linear algebra, probability or statistics and at least introductory programming.
• A CV or résumé outlining academic and professional experience.
• A personal statement describing your objectives, relevant experience and areas of interest within data science.
• Letters of recommendation from academic or professional referees who can speak to your technical aptitude and readiness for graduate study.
• International applicants are typically required to demonstrate English language proficiency if their prior education was not in English; specific accepted tests and score requirements are detailed by the university admissions office.

Career prospects

Graduates are prepared for a wide range of technical roles that require strong quantitative, programming and data-engineering skills. The programme emphasises practical, deployable solutions so alumni are ready for immediate contribution in professional settings.

• Typical roles: data scientist, machine learning engineer, data engineer, quantitative analyst, analytics consultant, business intelligence developer, and research analyst.
• Industries that commonly hire graduates: technology and software, healthcare and biotech, finance and insurance, manufacturing and supply chain, transportation and smart infrastructure, and government or public sector analytics units.
• Career development support: students benefit from project work with industry partners, internships, and the university's career services to build professional networks and transition into employment or doctoral study.

Why study at Florida Polytechnic University

Florida Poly is a specialised STEM-focused university with small class sizes and an applied approach to teaching and research. The environment emphasises hands-on learning, project-based courses and close faculty mentoring, which is well suited to technical master's study in data science.

• The university provides access to modern labs and computing resources, including support for GPU and cloud-based workflows used in advanced analytics and machine learning.
• Faculty bring a mix of academic and industry experience, enabling applied research and capstone projects that solve real organisational problems.
• Situated in Central Florida, the university has growing connections with regional technology firms, defence and aerospace companies, and research organisations, creating opportunities for collaboration, internships and employment.
• Small cohorts and focused curriculum mean personalised academic advising and the ability to tailor electives or projects to specific career goals or research interests.