Pittsburg State University

What you'll study

The Computational Mathematics pathway integrates core mathematical theory with numerical methods and scientific computing. Students complete foundational courses in calculus, linear algebra and differential equations before moving into specialised topics that emphasise computation, modelling and algorithmic implementation.

• Core mathematics: Calculus sequence, Linear Algebra, Ordinary Differential Equations, Advanced Calculus or Real Analysis.
• Computational and applied courses: Numerical Analysis/Numerical Methods, Scientific Computing, Numerical Linear Algebra, Computational Differential Equations, Mathematical Modelling and Simulation.
• Programming and software: Introduction to Programming (commonly using languages such as Python, MATLAB or C++), Data Structures and Algorithms, High‑Performance Computing concepts and use of mathematical software packages.
• Probability and statistics: Probability Theory, Mathematical Statistics and applied statistical methods for modelling and data analysis.
• Electives and breadth: Discrete Mathematics, Optimization, Complex Variables, Dynamical Systems, Machine Learning fundamentals, or courses from computer science and engineering departments to tailor the degree toward interests in data science, engineering or finance.
• Capstone and experiential learning: Senior capstone or research project involving a significant computational component, opportunities for undergraduate research with faculty, and internships or practicum placements with regional employers.

Entry requirements

Admission to the Bachelor of Science in Mathematics generally requires completion of secondary education with a strong background in mathematics. Applicants are expected to have studied algebra, geometry and precalculus or calculus; successful candidates typically demonstrate quantitative aptitude and problem‑solving ability.

• Secondary school completion or equivalent, with evidence of proficiency in mathematics.
• Preparation in calculus is strongly recommended; students without calculus may be advised to take placement or preparatory courses.
• Standardised test scores may be considered where applicable; transfers are evaluated on previous college coursework in mathematics and computing.
• Successful applicants often show additional strength through programming experience, coursework in science or computing, or participation in mathematics competitions or projects.

Career prospects

Graduates with a computational mathematics degree are well positioned for roles that require quantitative analysis, modelling and software skills. The combination of mathematical rigour and computational training opens career pathways across technology, finance, engineering and research.

• Data analyst, data scientist or machine learning technician in industry and public sector organisations.
• Quantitative or risk analyst roles in finance and insurance.
• Software developer or computational scientist working on simulation, scientific computing or optimisation problems.
• Operations research analyst, systems modeller or engineering analyst in manufacturing, energy and logistics.
• Further study in graduate programmes in mathematics, applied mathematics, computational science or data science and related doctoral research careers.

Why study at Pittsburg State University

Pittsburg State University offers a student‑centred environment with strong faculty engagement and opportunities for hands‑on learning. The Department of Mathematics provides accessible faculty mentors who supervise undergraduate research and capstone projects, and the curriculum emphasises applied skills that connect theory to real computational problems.

• Small class sizes and direct faculty interaction that support individual development and research involvement.
• Access to campus computing resources and labs, plus coursework that uses industry‑standard tools and programming languages.
• Opportunities for internships and applied experience with regional employers and collaborations across disciplines such as engineering, computer science and business.
• Active student organisations and academic support services that help students prepare for careers or graduate study.