Pace University

What you'll study

The programme builds a strong foundation in pure and applied mathematics while emphasising computational methods and scientific computing. Core subjects include multivariable calculus, linear algebra, differential equations and real analysis, together with probability and mathematical statistics.

• Numerical analysis and scientific computing: numerical linear algebra, numerical solution of ODEs and PDEs, error analysis and stability.
• Computational methods and programming: algorithm design, data structures, numerical programming in languages commonly used in scientific computing (such as Python, MATLAB and C/C++), and use of libraries for linear algebra and optimisation.
• Mathematical modelling: continuous and discrete modelling, optimisation, simulation techniques and model validation for applications in physics, engineering and finance.
• Statistics and data analysis: inferential statistics, regression, time series and introductory machine learning methods for analysing real datasets.
• Senior capstone or project: an applied computational project or research experience that integrates mathematical theory, numerical methods and software implementation.

Students also choose electives or cross-disciplinary courses from computer science, data science, or business to tailor the degree to interests such as quantitative finance, scientific computing, or data analytics. The curriculum typically includes general education requirements and opportunities for internships or cooperative placements.

Entry requirements

Admission to the Bachelor of Science in Mathematics (Computational Mathematics) normally requires a secondary school diploma or equivalent with strong preparation in mathematics. Typical expectations include:

• Good grades in high school mathematics, ideally including algebra, geometry and precalculus; completion of calculus (or equivalent) is strongly recommended.
• Preparation in science and computing is advantageous; applicants who have taken programming, AP/IB mathematics or advanced maths electives are preferred.
• Evidence of quantitative aptitude such as strong mathematics grades; a personal statement describing interest in computational mathematics and any relevant project or programming experience can strengthen an application.
• Where applicable, submission of transcripts and any standardised test scores required by the university. International applicants should meet the university's English language proficiency requirements.

Applicants with non-traditional backgrounds but demonstrable quantitative and programming skills may be considered; transfer students are evaluated on prior college coursework in mathematics and computing.

Career prospects

Graduates with a computational mathematics degree are well prepared for roles that require both mathematical rigour and programming skills. Typical career paths include:

• Data scientist or data analyst — building models, cleaning and analysing datasets, and implementing algorithms for insight and decision-making.
• Quantitative analyst (quant) in finance — developing pricing models, risk models and numerical methods for trading and investment.
• Scientific or software engineer — implementing numerical algorithms, simulation software and high-performance code for engineering and research applications.
• Actuarial analyst — applying probability, statistics and modelling in insurance and pension contexts (preparation for actuarial exams may be required).
• Graduate study and research — many students continue to master's or PhD programmes in applied mathematics, computational science, statistics or related fields.

Internships in the New York metropolitan area, industry collaborations and interdisciplinary projects often help graduates transition into technical roles across finance, technology, engineering and research organisations.

Why study at Pace University

Pace University offers access to a strong mathematics curriculum combined with practical computing skills and proximity to major employers in New York City. Students benefit from small to mid-sized classes, faculty who are active in teaching and applied research, and opportunities for hands-on learning through projects and internships.

• Interdisciplinary collaboration with computing and business programmes allows students to tailor the degree toward data science, finance or software development.
• Career services and local industry connections support internship placement and professional networking in the region.
• Facilities include computer labs and software resources commonly used in numerical and data-intensive work, enabling students to develop industry-relevant technical skills.

Overall, the programme is designed for students who want a mathematically rigorous degree with strong computational and applied components to prepare for technical careers or further study.