Chapman University

What you'll study

This programme emphasises core computational techniques and their application to problems in the natural sciences, engineering and data-rich domains. You will take foundational courses in calculus, linear algebra, probability and statistics alongside introductory and advanced programming (commonly using languages such as Python and C++). Core topics typically include numerical analysis and scientific computing, algorithms and data structures, computational modelling and simulation, machine learning, data visualisation, and high-performance computing.

• Mathematical foundations: calculus sequence, linear algebra, differential equations, probability and statistics.
• Programming and software: introductory programming, object-oriented programming, data structures, software development practices and version control.
• Computational methods: numerical methods, optimisation, scientific computing, parallel computing and simulation techniques.
• Data-focused modules: machine learning, statistical modelling, data mining and visual analytics.
• Domain applications: electives or modules applying computation to physics, biology, chemistry, engineering, finance or environmental science.
• Practical experience: laboratory work, project-based courses, and a senior capstone or research project that integrates modelling, implementation and evaluation.

The curriculum is typically delivered through a mix of lectures, small-group labs and project work, with opportunities for undergraduate research and industry-linked internships. Students are encouraged to build a portfolio of projects, including reproducible code, visualisations and technical reports.

Entry requirements

Admission to Chapman’s undergraduate programmes is selective and assesses academic preparation and fit for the field. Applicants should present a strong secondary-school record with substantial preparation in mathematics; prior coursework in calculus and exposure to programming are advantageous. Typical academic credentials considered include high school transcripts, completed mathematics courses and any computer science or quantitative coursework.

• US applicants: competitive high school GPA, completion of college-preparatory curriculum with mathematics through at least pre-calculus or calculus; demonstrated interest in computing or quantitative science strengthens an application.
• International applicants: equivalent secondary credentials (for example IB Diploma with higher-level mathematics, or A-levels with Mathematics and a science/computing subject) are expected.
• Other materials: personal statement, letters of recommendation and evidence of extracurricular engagement or project work in relevant areas can support an application. Standardised test submission policies vary; check Chapman’s admissions guidance for current details.

Career prospects

Graduates with a bachelor’s in Computational Science are prepared for roles that require quantitative modelling, software development and data analysis. Common career paths include:

• Computational scientist or research assistant in academia, industry or government laboratories
• Data scientist, data analyst or machine learning engineer in technology, finance, healthcare or consulting
• Software engineer or systems developer focused on scientific and engineering applications
• Quantitative analyst in finance and risk management
• Bioinformatics or computational biology roles in biotechnology and pharmaceutical companies

Many graduates also progress to graduate study (Master’s or PhD) in computational science, applied mathematics, computer science, statistics or domain sciences, or pursue professional pathways where computational skills are in demand.

Why study at Chapman University

Chapman’s Schmid College of Science and Technology provides an interdisciplinary environment with close faculty-student interaction and small class sizes, which benefits hands-on computational training. The university emphasises undergraduate research and offers access to modern computing facilities and laboratories where students can work on real-world projects under faculty supervision.

• Interdisciplinary opportunities: collaborative projects that span mathematics, computer science and applied sciences allow students to tailor their learning to specific interests.
• Undergraduate research and capstones: structured pathways for students to undertake research or industry-style capstone projects, often resulting in portfolio-ready outcomes.
• Location and connections: based in Orange County with proximity to the broader Southern California technology and biotech sectors, students have access to internship and employment opportunities in industry and research institutions.
• Support and mentoring: resources such as career services, faculty mentorship and student societies help students develop technical skills and professional networks.

Overall, Chapman’s computational science programme is suited to students seeking a practical, research-informed education that prepares them for computational roles across science, engineering and data-intensive industries.