Auburn University at Montgomery

What you'll study

This programme blends core mathematics with computational techniques and programming to prepare students for mathematically intensive careers. Typical core modules include calculus (single and multivariable), linear algebra, differential equations and real analysis to establish solid theoretical foundations.

• Numerical Analysis and Scientific Computing: methods for approximating solutions to equations, error analysis, stability and convergence, implemented in high-level languages.
• Computational Methods and Algorithms: algorithm design and analysis, complexity, numerical linear algebra, iterative methods and fast transforms.
• Mathematical Modelling and Applied Mathematics: building and analysing models from physics, engineering, biology and finance; simulation techniques.
• Probability, Statistics and Data Analysis: probability theory, statistical inference, regression, and introductory data science techniques for interpreting data.
• Programming for Mathematics: practical programming in languages commonly used in scientific computing (for example Python, MATLAB or C/C++), software tools and version control.
• Advanced Electives: topics such as optimization, partial differential equations, cryptography, discrete mathematics, and machine learning may be available depending on offerings.
• Capstone or Research Project: a supervised project applying computational and mathematical tools to a substantive problem, often involving coding, numerical experiments and written reporting.

The degree is structured to meet undergraduate credit requirements and includes general education courses alongside major requirements; students often take laboratory or practicum experiences and have opportunities for undergraduate research with faculty.

Entry requirements

Applicants normally need a high school diploma or equivalent with strong performance in mathematics. Typical expectations include successful completion of algebra, geometry and precalculus or calculus in secondary school. Admissions consider the overall academic record and preparation for university-level mathematics.

• Academic preparation: solid background in algebra and trigonometry; coursework in precalculus or calculus is advantageous.
• GPA and scores: a competitive high-school GPA is important; standardised test requirements vary and applicants should consult the university for current policies.
• Transfers: students transferring from community colleges should provide transcripts and documentation of completed mathematics courses; many transfer credits for equivalent calculus and introductory courses are accepted.
• Additional considerations: placement testing or departmental advising may be used to determine appropriate first-year mathematics courses; students without strong background may be advised to take preparatory coursework.

Career prospects

Graduates with a computational mathematics degree are prepared for a range of technical and analytical roles. Common career paths include:

• Data analyst or data scientist roles in business, healthcare and government, using statistical and computational tools to extract insights from data.
• Software development and engineering positions that require strong algorithmic thinking and numerical programming skills.
• Quantitative analyst or modelling roles in finance and insurance, often combined with further professional training or certification.
• Engineering and scientific computing positions supporting simulation, optimisation and computational modelling.
• Actuarial careers (often pursued with additional exams and professional credentials), and roles in operations research and optimisation.
• Secondary school or community college teaching with appropriate certification or graduate study; many graduates also continue to postgraduate study in applied mathematics, statistics, computer science or engineering.

Why study at Auburn University at Montgomery

Auburn University at Montgomery offers a close-knit learning environment with small class sizes and direct access to faculty with expertise in applied and computational mathematics. The department emphasises hands-on learning through laboratory classes, computing facilities and directed undergraduate research projects.

• Faculty support: opportunities for mentorship and collaborative research with faculty who work on applied modelling, numerical methods and interdisciplinary problems.
• Computing resources: access to modern computing labs and software used in scientific computing and data analysis.
• Experiential learning: internships and local industry or government collaborations in the Montgomery region provide practical experience.
• Preparation for next steps: strong preparation for careers in industry or for graduate study, with advising available for students pursuing professional certifications or advanced degrees.

Prospective students are encouraged to contact the department for detailed advising on course planning, research opportunities and how the programme can be tailored to specific career goals.