Eastern Michigan University

What you'll study

The Bachelor’s in Data Science is built around core computational, statistical and applied data courses, supplemented by electives and a capstone that emphasise hands-on work with real datasets. The curriculum balances foundational theory with practical tools used in industry and research.

• Foundations in computing and mathematics: programming fundamentals (Python and/or R), data structures and algorithms, discrete mathematics, linear algebra and calculus.
• Statistics and probability: inferential statistics, probability models, regression analysis and experimental design.
• Core data science: machine learning and predictive modelling, data mining, time series analysis, and statistical learning methods.
• Data engineering and systems: databases and SQL, data management, cloud computing concepts, and introduction to big-data frameworks (e.g. distributed processing concepts).
• Data visualisation and communication: information visualisation, dashboarding and communication of results for technical and non-technical audiences.
• Ethics, law and professional practice: data ethics, privacy, security and the societal impacts of data-driven decision making.
• Applied electives and domain courses: students select electives or minors in areas such as business analytics, health informatics, environmental data or engineering to apply methods to specific domains.
• Capstone / practicum: a year-long or semester-long team project that addresses a real-world problem in partnership with industry, campus units or faculty research, synthesising technical, communication and project-management skills.

Entry requirements

Admission to the undergraduate programme typically requires a high school diploma (or equivalent) with a strong foundation in mathematics. Prospective students should demonstrate preparedness for college-level mathematics and computing.

• Recommended academic background: high-school mathematics through precalculus or calculus and coursework in computer science or applied computing where available.
• Standardised tests: Eastern Michigan University assesses applications holistically; check the university’s admissions guidance for current test-optional policies.
• Minimum GPA: competitive applicants usually present a solid overall GPA in academic coursework; the university provides guidance on typical admitted student profiles rather than rigid cut-offs.
• International applicants: proof of English proficiency (such as TOEFL or IELTS) if required, plus evaluation of academic credentials from secondary education institutions.
• Transfer students: transferrable college credits in mathematics, statistics or programming can be applied toward the degree; transfer admission requirements follow university guidelines.

Career prospects

Graduates of the Data Science programme enter a wide range of roles where employers need people who can extract insight from data and implement systems that support data-driven decisions.

• Data analyst, business intelligence analyst and reporting specialist
• Data scientist and machine learning practitioner (entry-level roles following solid applied experience)
• Data engineer, ETL developer and database administrator
• Analytics consultant and product/operations analyst in sectors such as finance, healthcare, manufacturing, retail and public sector
• Roles in research support, data-driven policy and technology start-ups; some graduates continue to graduate study in data science, statistics, computer science or related fields

Why study at Eastern Michigan University

Eastern Michigan University offers small-to-medium class sizes that allow close interaction with faculty who teach applied computing and statistics. The university emphasises experiential learning, providing opportunities for undergraduate research, internships with local and regional employers, and community-engaged projects.

• Applied focus: coursework and the capstone emphasise hands-on projects with real datasets and tools commonly used in industry.
• Interdisciplinary pathways: students can combine the degree with minors or electives across business, health, engineering or social sciences to tailor career preparation.
• Access to resources: computing labs, data-focused student organisations, career services and industry partnerships help students build portfolios and secure internship placements.
• Supportive transition: advising and tutoring services assist students moving from high-school mathematics and programming into more advanced data science topics.