Florida State University

What you'll study

The programme blends core topics in computing, statistics and applied mathematics with applied work in data engineering and scientific computing. Typical taught modules cover:

• Foundations of Data Science: probability, statistical inference and exploratory data analysis.
• Machine Learning and Statistical Learning: supervised and unsupervised methods, model selection and evaluation.
• Data Management and Systems: databases, data wrangling, data pipelines and basic distributed systems concepts.
• Algorithms and Computational Methods: algorithm design, optimisation, numerical methods and scalable algorithms for large data.
• High-Performance and Parallel Computing: parallel programming, GPU computing and use of university research computing facilities for large-scale processing.
• Data Visualisation and Communication: visual analytics, dashboarding and communicating results to technical and non-technical audiences.
• Electives and Domain Applications: students may choose electives in areas such as natural language processing, computer vision, computational biology, geospatial analytics or finance.
• Capstone Project or Thesis: a substantial practicum, industry project or research thesis that applies methods to real-world or research problems under faculty supervision.

Programme structure typically comprises a mix of core courses, electives and a final project or thesis. Students gain hands-on experience with programming languages and tools commonly used in the field (for example Python, R, SQL and parallel computing frameworks), and have opportunities to work with faculty on research or industry-collaborative projects.

Entry requirements

Applicants are expected to hold a recognised bachelor's degree in computer science, mathematics, statistics, engineering, physics or a closely related quantitative discipline. Typical admissions criteria include:

• A competitive undergraduate academic record (GPA criteria follow university standards).
• Demonstrable background in programming and familiarity with data structures and algorithms.
• Foundational knowledge of calculus and linear algebra and some coursework or experience in probability and statistics.
• Submission materials: official transcripts, statement of purpose outlining goals and relevant experience, curriculum vitae or résumé, and letters of recommendation.
• International applicants must meet the university's English language proficiency requirements.

Some applicants with strong quantitative experience but non-traditional backgrounds may be admitted conditional on completing prerequisite coursework. Standardised test requirements vary; consult the programme admissions pages for current policy.

Career prospects

Graduates are prepared for a wide range of roles in industry, government and research. Common career paths include:

• Data scientist or senior data analyst, building predictive models and deriving actionable insights from data.
• Machine learning engineer, developing and deploying machine learning systems at scale.
• Data engineer, designing and maintaining data pipelines and storage solutions.
• Research scientist in computational disciplines, working in universities, national labs or R&D teams.
• High-performance computing specialist, optimising scientific codes and workflows for parallel environments.
• Domain-specific analytics roles in healthcare, finance, energy, geosciences and public sector agencies.

Graduates benefit from practical project experience and faculty connections to secure internships and employment with regional and national employers, start-ups and research organisations.

Why study at Florida State University

Florida State University offers an interdisciplinary environment with faculty working across computer science, statistics, engineering and domain sciences, making it well suited to data science training that spans theory and application. Students have access to university research computing resources and opportunities to collaborate with faculty research centres focused on scientific computing and data-intensive research. The programme emphasises hands-on projects, allowing students to build a professional portfolio and engage with local industry partners and research labs. Located in Tallahassee, the university provides connections to state agencies and regional employers alongside vibrant campus life and student support services for graduate students.