Michigan Technological University

What you'll study

The curriculum combines foundations in mathematics and computer science with applied statistics, data engineering and machine learning. Core topics typically include probability and mathematical statistics, linear algebra, statistical inference, data structures and algorithms, database systems, data visualisation, and supervised and unsupervised learning methods. Coursework emphasises practical data workflow skills—data acquisition and cleaning, exploratory analysis, feature engineering, model evaluation and reproducible reporting.

• Foundations: calculus, linear algebra, discrete mathematics and introductory programming (often Python and/or R).
• Statistics and probability: introductory and intermediate statistics, regression, experimental design and statistical computing.
• Computer science and data systems: data structures, algorithms, databases, data management and cloud or distributed computing concepts.
• Machine learning and analytics: supervised and unsupervised learning, classification, clustering, time series analysis and model validation.
• Data visualisation and communication: dashboards, interactive visualisation, storytelling with data and technical writing for stakeholders.
• Capstone and experiential learning: team-based projects, industry-sponsored capstones, internships and opportunities to work with faculty research groups.

Electives allow specialisation in areas such as big data engineering, natural language processing, computational statistics, business analytics, or domain-specific analytics in engineering, health or environmental science.

Entry requirements

Applicants are typically expected to hold a high-school diploma or equivalent with a strong background in mathematics. Successful candidates usually demonstrate proficiency in algebra and calculus and have some exposure to programming or computer science. Admissions consider a combination of academic record, recommendations and any relevant extracurricular experience such as coding projects or math competitions.

International applicants must meet university language requirements. Transfer students are evaluated on previous post‑secondary coursework; credit for introductory maths, science and computing classes may be awarded based on course content and grades. Standardised tests may be optional depending on the university's current admissions policies—check the university’s admissions pages for the latest details.

Career prospects

Graduates enter a wide range of data‑focused roles in industry, government and research. Typical job titles include data analyst, data scientist, business intelligence analyst, data engineer, machine learning engineer and analytics consultant. Employers span sectors such as manufacturing and industrial analytics, automotive, energy and utilities, healthcare analytics, finance, and technology companies.

Many students pursue internships during their studies, and capstone projects often lead to direct hiring. Graduates may also continue to postgraduate study in data science, statistics, computer science or specialised engineering domains.

Why study at Michigan Technological University

Michigan Tech is known for its strong engineering and computing emphasis and for integrating hands‑on, project-based learning into undergraduate degrees. Students benefit from access to research faculty, computing facilities and multidisciplinary centres working on applied data problems. The university’s cooperative industry relationships and regional ties to manufacturing, mining, energy and technology firms create internship and employment pathways for analytics graduates.

Beyond academics, Michigan Tech emphasises experiential education—enterprise projects, student research opportunities and community partnerships provide real-world datasets and challenges. The campus environment supports close collaboration between students and faculty and offers an active student community and outdoor lifestyle in the Great Lakes region.