Wartburg College

What you'll study

This programme blends mathematics, statistics, computer science and business to teach students how to build and apply quantitative models for decision making. Coursework emphasises analytic thinking, applied computation and real-world problem solving through projects and internships.

• Core quantitative foundations: calculus, linear algebra, probability and mathematical statistics that provide the theoretical basis for modelling and inference.
• Applied statistics and data analysis: courses in regression, time series, multivariate analysis, and data visualisation, with hands-on work in statistical software (for example R and Python).
• Operations research and optimisation: deterministic and stochastic optimisation, simulation, queuing models and decision analysis for operations and supply-chain problems.
• Management and business fundamentals: introductory and intermediate courses in microeconomics, accounting, management and organisational behaviour to link quantitative results to managerial decisions.
• Computing and data management: programming for data analytics, database systems, and coursework in data wrangling and reproducible research.
• Integration and practice: a senior capstone or practicum where students complete a semester-long project for a real organisation, and options for credit-bearing internships.
• Electives and interdisciplinary options: ability to select electives in finance, marketing analytics, actuarial studies, information systems or related areas, and to combine studies with minors across the liberal arts.

Programme structure

Students complete a mix of core requirements in quantitative methods, business-related courses, liberal-arts general education, and elective credits. The curriculum typically culminates in a capstone experience or internship that demonstrates applied competence in quantitative analysis and management decision making.

Entry requirements

Applicants are expected to hold a recognised secondary-school diploma or equivalent with a competitive academic record. Successful candidates typically demonstrate strong preparation in mathematics (algebra and pre-calculus or calculus) and show evidence of analytical ability.

• Recommended preparation: high-school mathematics through calculus or advanced algebra and coursework in statistics if available.
• Admissions materials: official secondary-school transcript and any supporting statements or recommendations as required by the college.
• Transfer students: college transcripts and descriptions of previously completed coursework are reviewed for transfer credit; relevant community-college coursework in mathematics, statistics or computer science is often accepted.
• Other considerations: a demonstrated interest in quantitative problem solving, computer programming experience, or participation in relevant extracurricular activities can strengthen an application.

Career prospects

Graduates enter a wide range of careers that value analytical thinking and quantitative skills. The programme prepares students for roles that combine data, modelling and management insight.

• Common entry-level roles: data analyst, operations analyst, business analyst, financial analyst, supply-chain analyst or research analyst.
• Other pathways: positions in consulting firms, healthcare analytics, marketing analytics, and roles supporting technology and information systems.
• Professional development: many graduates go on to graduate study in business, statistics, data science, operations research, economics or related fields; others pursue professional certifications in analytics or project management.
• Employers: graduates find work in private sector firms, non-profit organisations, government agencies and start-ups where quantitative decision making is needed.

Why study at Wartburg College

Wartburg College offers a liberal-arts environment with small class sizes and close faculty mentoring, which benefits students learning technical and applied subjects. The college emphasises experiential learning—students often complete internships, community projects and capstone experiences that build workplace-ready skills.

• Faculty support: accessible professors who supervise research projects and advise on graduate-school and career plans.
• Hands-on experience: opportunities for internships, consulting projects with local organisations, and applied coursework using real datasets.
• Cross-disciplinary education: the ability to pair quantitative training with courses in business, computer science and liberal arts for well-rounded problem solvers.
• Career services: resources for resume development, interview preparation and connections to employers through campus recruiting and alumni networks.