University of North Florida

What you'll study

The BSc in Data Analytics blends foundational coursework in mathematics and computing with applied subjects in statistics, machine learning and data management. The curriculum balances theoretical understanding with practical experience using contemporary tools and platforms.

• Core computing and programming: introduction to programming (commonly Python or equivalent), data structures, software development practices and version control.
• Statistics and quantitative methods: probability, statistical inference, regression analysis and experimental design.
• Data management: relational databases, SQL, data modelling, data cleaning and preparation.
• Analytics and machine learning: supervised and unsupervised learning, predictive modelling, classification, clustering and evaluation of models.
• Data visualisation and communication: visual analytics, dashboard development (tools such as Tableau or similar), and techniques for presenting findings to non-technical audiences.
• Big data and modern tooling: introduction to distributed data processing, cloud-based analytics and common libraries/frameworks used in industry (for example, pandas, scikit-learn, Spark).
• Ethics and legal considerations: data privacy, responsible use of data and issues of bias and fairness in analytics.
• Capstone and experiential learning: a senior project or practicum that typically involves working on a real dataset for an external partner, as well as opportunities for internships with regional businesses and public-sector organisations.

Entry requirements

Applicants are expected to meet the University of North Florida's undergraduate admission standards and demonstrate readiness for college-level mathematics and computing. Typical requirements include:

• US applicants: high school diploma or equivalent with a record of coursework in mathematics (algebra, precalculus or calculus recommended). Standardised test submission policies may vary; check the university admissions pages for current guidance.
• International applicants: an equivalent secondary school qualification and proof of English proficiency via an accepted test (for example, TOEFL or IELTS) unless exempt.
• Transfer students: transferable college credit with satisfactory GPA and completion of prerequisite courses in mathematics or introductory programming may be required.
• Additional considerations: a demonstrated interest in computing, mathematics or statistics (through coursework, personal projects or relevant extracurricular activity) can strengthen an application. Specific prerequisite courses and minimum GPA expectations are set by the university and may vary, so applicants should review departmental admission information.

Career prospects

Graduates with a degree in Data Analytics enter a broad and growing job market. The programme prepares students for roles that require analytical thinking, technical skills and the ability to communicate insights.

• Data Analyst / Business Intelligence Analyst: analysing datasets, building reports and dashboards to support business decisions.
• Data Scientist (entry-level): applying predictive models and machine learning methods; many graduates continue professional development or postgraduate study to advance in this path.
• Data Engineer (junior roles): building and maintaining data pipelines, working with databases and cloud services.
• Analytics roles in sector-specific contexts: finance, healthcare, logistics, marketing, public sector and sports analytics, among others.
• Consultancy and operations research: advisory roles that translate data insights into strategic or operational improvements.

Many graduates find internships and employment with regional businesses, healthcare providers, financial institutions and government agencies in the Jacksonville area and beyond. Graduates may also choose to pursue graduate study in data science, statistics, computer science or related fields.

Why study at University of North Florida

The University of North Florida offers a programme designed with an emphasis on applied, hands-on learning and connections to the Jacksonville business and public-sector community. Students benefit from reasonably sized classes, opportunities for undergraduate research and project work, and access to modern computing facilities.

• Practical, project-based learning: capstone projects and practicum experiences allow students to work on real datasets and problems with external partners.
• Local industry links: proximity to a diverse regional economy provides internship and employment opportunities with companies, hospitals and government organisations.
• Supportive campus environment: academic advising, career services and student organisations focused on computing and analytics help students develop professional skills and networks.
• Pathways to further study: the programme equips students with the quantitative and technical foundations needed for graduate study in data science, statistics or computer science if they wish to specialise further.