Florida Polytechnic University

What you'll study

This programme develops core skills in programming, statistical modelling, machine learning and data engineering alongside the mathematical foundations that underpin quantitative analysis. Teaching is hands-on and project-based, with laboratory work, team projects and a capstone integrating real-world data problems.

• Core programming and software: Python and/or R programming, data structures and algorithms, software development practices, version control and scripting for data workflows.
• Mathematics and statistics: calculus, linear algebra, probability theory, statistical inference and applied statistics for data analysis.
• Data-focused modules: databases and SQL, data wrangling and cleaning, data visualisation, exploratory data analysis and reporting.
• Machine learning and modelling: supervised and unsupervised learning, model evaluation, feature engineering, deep learning fundamentals and practical model deployment.
• Big data and systems: scalable data processing, cloud computing basics, distributed systems, and tooling for handling large datasets.
• Ethics and professional practice: data ethics, privacy and security, legal considerations and responsible use of algorithms.
• Capstone and experiential learning: a culminating team-based project that applies classroom learning to an external or internally sourced problem; options for internships or co-op placements with industry partners.
• Electives and special topics: opportunities to take elective modules in areas such as computer vision, natural language processing, business analytics, optimisation or domain-specific applications (e.g. healthcare, finance, IoT).

Entry requirements

Applicants are expected to have completed secondary education with a strong emphasis on mathematics. Admissions typically look for experience or demonstrated aptitude in calculus or advanced algebra; programming experience is advantageous but not always required. Candidates should be comfortable with quantitative problem-solving and have a good grounding in scientific thinking.

• Typical academic background: secondary school diploma with strong marks in mathematics (pre-calculus or calculus) and science subjects. For applicants coming from different curricula, equivalent qualifications demonstrating quantitative competence are considered.
• Recommended skills: familiarity with at least one programming language (such as Python, Java or C++), basic statistics, and logical reasoning.
• International applicants: must meet English language proficiency requirements through recognised tests or approved alternatives; equivalency of academic qualifications will be assessed.
• Additional considerations: prospective students may strengthen applications with relevant extracurricular experience (coding clubs, data competitions, research projects) or with prior college coursework in mathematics or computing.

Career prospects

Graduates are prepared for a wide range of technical and analytical roles in private industry, government and research. The applied nature of the programme and industry-facing capstone projects help students build portfolios that employers value.

• Data Scientist – developing predictive models, statistical analyses and machine learning solutions.
• Data Analyst / Business Intelligence Analyst – transforming data into operational insights and visual reports for decision-making.
• Machine Learning Engineer – implementing and deploying scalable machine learning systems.
• Data Engineer – building pipelines, ETL processes and managing storage and processing of large datasets.
• Software Engineer or Developer roles with a focus on data-driven applications.
• Opportunities in specialised domains such as finance, healthcare analytics, supply chain optimisation, IoT analytics and government analytics.
• Preparation for postgraduate study in data science, computer science, statistics or related engineering disciplines.

Why study at Florida Polytechnic University

Florida Polytechnic University is a technology-focused institution with a curriculum designed around applied STEM learning. The university emphasises hands-on projects, lab-based teaching and close engagement with industry partners, which helps students translate classroom theory into practical skills.

• Project-based curriculum: coursework and capstone projects are oriented to real-world problems, giving students tangible portfolio work.
• Small, focused campus: a STEM-dedicated community with opportunities for close collaboration with faculty and peers and accessible research and lab facilities.
• Industry connections: partnerships with regional and national technology companies provide internship and employment pathways and frequent guest lectures and project sponsorships.
• Modern facilities and computing resources: access to high-performance computing, specialised software, and labs geared to data-intensive work.
• Career support: dedicated career services and experiential learning offices that help students secure internships, co-op placements and entry-level roles in analytics and technology.