Florida State University

What you'll study

The programme combines core quantitative and computational subjects with applied projects to prepare you for real-world data work. Typical core topics include probability and statistical inference, applied regression and predictive modelling, machine learning, data mining, and time series analysis. Computational and engineering content covers programming for data science (Python and R), database systems and SQL, distributed and big-data technologies, and data visualisation.

Students usually complete a mix of required core courses and electives drawn from areas such as natural language processing, deep learning, spatial analytics, optimisation, and data ethics and privacy. The degree emphasises hands-on experience: most students undertake a practicum or capstone project with a real dataset, and there are options for research or a project-based thesis depending on the programme track.

Entry requirements

Applicants normally need a recognised bachelor’s degree. Competitive applicants hold degrees in statistics, mathematics, computer science, engineering, economics or other quantitative disciplines and demonstrate strong quantitative and programming foundations. Admissions materials typically include academic transcripts, a statement of purpose, and a curriculum vitae or résumé.

FSU considers prior coursework in calculus, linear algebra, probability or statistics, and at least one programming language to be important preparation. The programme may request letters of recommendation and may consider standardised test scores where provided; international applicants must demonstrate English language proficiency through an approved test unless exempted by the university.

Career prospects

Graduates go on to roles that apply data-driven decision making across sectors. Typical job titles include data analyst, data scientist, business intelligence analyst, machine learning engineer, and analytics consultant. Employers span technology firms, financial services, healthcare and pharmaceuticals, public-sector agencies, utilities, and consulting firms.

The curriculum’s applied focus and capstone experience are designed to build a professional portfolio of projects and practical skills valued by employers, such as model development, data engineering workflows, visualisation, and communication of analytic results to non-technical stakeholders.

Why study at Florida State University

Florida State University offers a campus environment with interdisciplinary connections across statistics, computer science, business and public policy, enabling collaboration on applied analytics problems. Students benefit from faculty engaged in applied and methodological research, access to computing resources and data labs, and opportunities to work with local industry and government partners in Tallahassee.

FSU’s career services and engineering/informatics networks support internships and employer engagement, helping students transition from study to professional roles. The university’s location and partnerships also provide practical avenues for practicum projects and experiential learning in real organisational settings.