Case Western Reserve University

What you'll study

This programme builds a core foundation in computation, mathematics and statistics, then moves into applied data science topics and domain electives. Early coursework typically covers programming and software development, data structures and algorithms, calculus and linear algebra, and introductory probability and statistics. Intermediate and advanced modules commonly include machine learning, statistical modelling, database systems, data visualisation, data mining, and big data technologies. Ethical, legal and societal aspects of data use are integrated through classes on data ethics and responsible AI.

Students take laboratory and project courses that emphasise practical experience with real datasets, reproducible workflows and collaborative software development. A capstone or senior design project is a core element, where teams work on an applied data science challenge often in partnership with industry, healthcare providers or research groups. Electives allow specialisation in areas such as computational biology and bioinformatics, natural language processing, computer vision, finance, or social data analysis.

• Foundations: introductory programming, data structures, discrete mathematics, calculus, linear algebra, probability and statistics
• Core data science: databases, data visualisation, statistical inference, machine learning, data mining, scalable data systems
• Applied experience: labs, practicum placements or internships, team projects, senior capstone
• Domain electives: bioinformatics, medical data analytics, NLP, computer vision, business analytics
• Professional skills: scientific communication, software engineering practices, ethics and policy for data practitioners

Entry requirements

Applicants are expected to have strong preparation in mathematics and science from secondary school. Typical preparation includes calculus (or precalculus progressing to calculus), courses in algebra and geometry, and experience with programming or problem solving; AP, IB or other university preparatory courses in calculus and computer science are advantageous. Admissions decisions consider the overall academic record, letters of recommendation, personal statement and any relevant extracurricular experience such as coding projects, research or internships.

International applicants must demonstrate equivalent secondary qualifications and English language proficiency as required by the university. Prospective students should consult the university admissions pages for specific credential and documentation requirements, as well as guidance on course equivalencies.

Career prospects

Graduates emerge prepared for roles that require strong quantitative and computational skills and the ability to communicate results to non‑technical stakeholders. Common job titles include data scientist, data analyst, machine learning engineer, business intelligence analyst, data engineer and software developer. The programme’s applied projects and industry partnerships also prepare graduates for sector‑specific roles in healthcare analytics, finance, technology and consulting.

Many students pursue graduate study in data science, computer science, statistics or related fields. The degree also provides a useful foundation for professional certification and continued upskilling in specialised tools and machine learning frameworks.

Why study at Case Western Reserve University

Case Western Reserve University combines a research‑intensive engineering school with strong ties to Cleveland's healthcare and technology sectors, offering students access to interdisciplinary research, clinical datasets and industry internships. The university emphasises experiential learning through hands‑on labs, undergraduate research opportunities and capstone projects that connect students with faculty and external partners.

Students benefit from small class sizes in advanced courses, faculty active in applied and theoretical research, and university career services that support internship placement and employer engagement. The Cleveland location provides a regional ecosystem of hospitals, startups, and established companies that recruit data‑trained graduates for roles across healthcare, finance, manufacturing and tech.

Overall, the programme is designed to deliver rigorous technical training together with practical experience and domain flexibility, preparing graduates to tackle real‑world data challenges or to continue on to specialised graduate study.