University of Central Oklahoma

What you'll study

The curriculum combines core computational methods, applied mathematics and practical programming with domain-specific electives. Students typically study numerical analysis, scientific computing, parallel and high-performance computing, data visualisation and modelling and simulation. Coursework emphasises hands-on implementation in common scientific programming environments and languages, such as Python, C/C++ and parallel frameworks, and covers topics like machine learning for scientific datasets, uncertainty quantification and algorithm optimisation.

• Core topics: numerical methods, scientific computing, linear algebra for computation, and algorithm development
• High-performance computing: parallel programming, GPU computing, cluster computing and performance tuning
• Data-focused modules: data analysis, data visualisation and machine learning for scientific applications
• Modelling and simulation: mathematical modelling, simulation frameworks and verification/validation
• Project work: capstone project, research thesis option or applied practicum working on an interdisciplinary problem

Entry requirements

Applicants should hold a bachelor’s degree in computer science, mathematics, physics, engineering, or another quantitatively focused discipline. The programme expects competency in calculus and linear algebra and prior programming experience; applicants who lack specific prerequisites may be asked to complete remedial courses.

• Academic transcripts showing a relevant undergraduate qualification
• A personal statement describing research or professional goals and fit with the programme
• Letters of recommendation and a current CV or resume
• International applicants must demonstrate English language proficiency according to university regulations

The university may consider additional materials such as a portfolio of computational work or prior research experience. Specific admission conditions and any standardised test requirements are determined by the graduate admissions office and can vary by applicant.

Career prospects

Graduates are prepared for roles that require advanced computational skills across industry, government and academia. Typical career paths include computational scientist, data scientist/analyst, scientific software developer, high-performance computing engineer and quantitative modeller. Employers include research laboratories, energy and aerospace companies, finance and insurance firms, healthcare and biotech organisations, and technology companies focused on simulation and analytics.

Alumni may also continue in doctoral study or take applied research roles in universities and national research centres where multidisciplinary computational expertise is valued.

Why study at University of Central Oklahoma

The University of Central Oklahoma offers a supportive, small-cohort environment that emphasises close faculty mentoring and applied learning. Students benefit from access to university computing resources and labs, opportunities to collaborate with faculty on research projects, and interdisciplinary connections across science and engineering departments.

Located in a region with active industry partners, the programme facilitates applied projects and practicum experiences with local employers and research organisations. Graduate students can explore assistantships and departmental opportunities that provide research experience and professional development while completing their degree.