Trinity University

What you'll study

The programme builds a strong foundation in pure and applied mathematics while emphasising numerical methods and computational tools. Core topics include calculus, linear algebra, differential equations and real analysis, alongside dedicated courses in numerical analysis, scientific computing and computational linear algebra.

• Calculus sequence and multivariable calculus
• Linear algebra and matrix theory
• Differential equations (ordinary and partial)
• Real analysis and mathematical proof techniques
• Numerical methods and error analysis
• Scientific computing with high-level languages (e.g. Python, MATLAB)
• Algorithms and data structures
• Probability, statistics and stochastic modelling
• Optimization and numerical linear algebra
• Computational modelling of physical and biological systems
• Electives such as machine learning, data science, computational fluid dynamics, or cryptography
• Capstone project or senior thesis involving applied computation and modelling

The degree typically spans four years and combines lectures, small-group seminars and laboratory-style computing sessions. Students are encouraged to take complementary courses in computer science, physics, engineering or economics and to participate in undergraduate research and summer internships.

Entry requirements

Applicants should hold a secondary school qualification appropriate for university entry and demonstrate strong preparation in mathematics. Typical successful applicants have completed pre-calculus and calculus courses; further credit or advanced standing may be awarded for higher-level qualifications in mathematics.

• Academic transcripts showing solid performance in mathematics and quantitative subjects
• Personal statement outlining quantitative interests and any programming or research experience
• One or more academic references (often from a teacher in mathematics or a related subject)
• For international applicants, documentation of equivalent secondary qualifications and evidence of English language proficiency where required
• Transfer applicants: college transcripts and a summary of prior coursework for credit evaluation

While test scores and specific admissions processes vary, applicants with prior programming experience (Python, MATLAB, C/C++) or coursework in computer science will be well prepared for the computational emphasis of the programme.

Career prospects

Graduates in Computational Mathematics are equipped for roles that combine quantitative reasoning and computing. Career paths include:

• Data scientist or data analyst in technology, healthcare, finance and government
• Quantitative analyst or risk modeller in banking and finance
• Software engineer with a focus on numerical libraries or scientific applications
• Computational scientist or modeller in engineering, physical sciences and bioinformatics
• Actuarial analyst or statistics-focused roles
• Graduate study in applied mathematics, statistics, computer science, engineering or related fields

Students also benefit from internships, undergraduate research and career services that help translate technical skills into industry placements or graduate opportunities.

Why study at Trinity University

Trinity University offers small class sizes and close faculty mentorship that suits students seeking intensive undergraduate training in both theory and computation. The department emphasises hands-on experience with modern computational tools, accessible research opportunities for undergraduates and interdisciplinary collaboration across computer science, physics and engineering.

• Opportunities for one-on-one supervision on senior projects and research
• Access to computing facilities and software used in industry and research
• Flexible curriculum that lets students combine mathematics with data science, engineering or economics
• Support for internships and career development through institutional career services and employer relationships

Together these features prepare graduates to apply mathematical insight and computational skill to complex, real-world problems in a variety of professional and academic settings.