Clarkson University

What you'll study

The programme integrates core subjects in applied mathematics, statistics, computer science and engineering, with an emphasis on computational methods and data-driven decision making. Early courses typically cover calculus, linear algebra, probability and statistics, and an introduction to programming. Core data science topics include data structures and algorithms, databases, data mining, machine learning, numerical methods, and scientific computing.

Students also study engineering-focused applications such as signal and image processing, optimization, computational modelling and simulation, and systems analysis. Coursework is reinforced through laboratory classes, programming projects and team-based assignments. The curriculum culminates in a capstone or senior design project that requires students to solve a practical problem using end-to-end data science workflows—data acquisition, cleaning, modelling, validation and presentation.

• Foundational mathematics: calculus, linear algebra, numerical analysis
• Statistics and probability for data analysis
• Core computer science: programming, data structures, algorithms
• Databases, data engineering and data management
• Machine learning, pattern recognition and predictive modelling
• Scientific computing, simulation and applied optimisation
• Domain electives aligned with engineering, physical sciences, business or health
• Capstone project, research opportunities, and internship/co‑op experiences

Programme structure

The degree balances required core courses with elective options that allow students to specialise toward applications in engineering, environmental science, finance, health or business. Hands-on learning is emphasised through lab courses, undergraduate research with faculty, and internship or cooperative education placements that give industry experience.

Entry requirements

Applicants should hold a secondary-school qualification with strong preparation in mathematics. Typical preparation includes calculus and advanced mathematics; prior exposure to programming or computer science is advantageous. Admissions decisions consider the overall academic record, mathematics grades, coursework in science or computing, and any relevant project or work experience.

International applicants must demonstrate English language proficiency through recognised testing or prior education in English, and provide documentation equivalent to the university's secondary-school expectations. Transfer students from other institutions are considered on the basis of college transcripts and alignment of previous coursework with programme requirements.

Career prospects

Graduates are prepared for technical and analytical roles across industry, government and academia. Common career paths include data scientist, data engineer, machine learning engineer, business intelligence analyst, software engineer and quantitative analyst. The programme also provides a strong foundation for further study in graduate programmes such as computer science, statistics, data science, engineering or applied mathematics.

Because the degree combines computing with engineering applications, alumni find opportunities in sectors such as manufacturing and industrial technology, energy and environment, finance and insurance, healthcare and biotechnology, transportation and autonomous systems, and consulting. Internship and co‑op experiences help students build professional networks and practical skills valued by employers.

Why study at Clarkson University

Clarkson provides a hands-on, applied approach to data science education with small class sizes and close faculty mentoring. The university emphasises interdisciplinary collaboration, allowing students to pair computational expertise with engineering, science or business domains. Students benefit from access to research labs, computing resources and project-based learning that mirror industry practice.

The institution also supports experiential learning through internship and co‑op programmes and maintains connections with regional and national employers, which helps students secure real-world placements. Faculty engaged in applied research offer opportunities for undergraduates to contribute to projects, and career services provide guidance on internships, employer engagement and graduate-school preparation.