University of Central Oklahoma

What you'll study

This programme builds a foundation in calculus, linear algebra and probability before progressing to core data science topics. You will study programming (commonly Python and R), data structures, databases and software engineering practices alongside statistical inference, regression, experimental design and time series analysis.

• Foundational courses: Calculus, Linear Algebra, Introductory Statistics, Discrete Mathematics.
• Computer science and programming: Introduction to Programming, Data Structures and Algorithms, Database Systems, Software Development and Computational Thinking.
• Core data science: Statistical Inference, Machine Learning, Data Mining, Predictive Modelling, Applied Regression, Time Series and Multivariate Analysis.
• Data engineering and tools: Big Data concepts, Data Wrangling, ETL principles, Cloud computing basics and working with data platforms and APIs.
• Applied and domain coursework: Capstone project or practicum, data visualisation, ethics in data science, and electives that let you apply methods in business, health, social sciences or engineering contexts.

The programme typically culminates in a senior capstone or practicum project in which you work on a real-world dataset, often in collaboration with a faculty member or industry partner. Coursework emphasises hands-on lab work, reproducible workflows, and communication of results to non-technical audiences.

Entry requirements

Applicants are normally expected to hold a high-school diploma or equivalent with strong performance in mathematics. Typical preparation includes algebra, precalculus or calculus, and coursework that demonstrates quantitative aptitude.

• Academic background: Successful completion of college-preparatory mathematics; prior coursework in computer science is advantageous but not always required.
• Grade expectations: Admission decisions consider overall academic record; competitive applicants show strength in maths and related subjects.
• Transfer students: Transfer applicants may satisfy some lower-division requirements with community college credits; official transcripts are required for evaluation.
• English proficiency: International applicants must demonstrate English language proficiency through recognised tests or prior study in English, according to university policy.
• Additional considerations: Some applicants include programming samples, a personal statement describing interest in data science, or letters of recommendation to strengthen their application.

Career prospects

Graduates leave prepared for roles that require quantitative analysis, data management and predictive modelling. The programme equips students for direct entry into data-focused positions as well as graduate study.

• Data Scientist / Machine Learning Analyst
• Data Analyst / Business Intelligence Analyst
• Data Engineer or Database Specialist
• Quantitative Analyst in finance, healthcare or energy sectors
• Research assistant in academic or government research groups
• Technology consultant or analytics professional in industry-specific roles

UCO graduates also commonly pursue further study in statistics, computer science, data science or specialised professional programmes. Practical experience gained through internships, capstone projects and campus career services is a strong pathway into local and regional employers.

Why study at University of Central Oklahoma

University of Central Oklahoma offers a programme that balances theoretical foundations with applied skills. The university’s location near the Oklahoma City metropolitan area gives students access to internship and employment opportunities across government, energy, healthcare and technology employers.

• Applied learning: Emphasis on lab courses, capstone projects and practicum partnerships that let students work with real datasets and stakeholders.
• Faculty and support: Instruction from faculty with experience in mathematics, statistics and computer science, plus academic advising and structured support for quantitative coursework.
• Facilities and resources: Access to computing labs, data visualisation tools and campus services that support research, coding practice and portfolio development.
• Career development: Active career services, employer networking events and close ties to regional employers that help students secure internships and entry-level roles.

The programme is suited to students seeking a practical, career-oriented education in data science with opportunities to tailor study through electives and applied projects.