Michigan Technological University

What you'll study

The programme blends theoretical and applied coursework in mathematics, statistics, computer science and data engineering. Early study focuses on foundational topics such as calculus, linear algebra, probability and introductory programming. Core data science topics include statistical inference, regression and experimental design, machine learning, data mining, data visualisation, databases and large-scale data management.

Students also learn software engineering practices, algorithms, and computational modelling to support production-ready data products. Applied coursework and laboratories emphasise hands-on experience with programming languages and tools commonly used in the field, such as Python, R, SQL and data processing frameworks. Ethical considerations and data governance are integrated into the curriculum to prepare students for responsible practice.

The degree typically culminates in an applied capstone or senior design project in which teams address an authentic data problem in collaboration with faculty, industry partners or research groups. Electives allow specialisation in areas such as deep learning, natural language processing, time-series analysis, scientific computing, high-performance computing, or domain-specific applications in engineering and the natural sciences.

• Foundations: Calculus, Linear Algebra, Probability and Statistics
• Core computing: Data Structures, Algorithms, Software Engineering
• Data-focused courses: Statistical Inference, Machine Learning, Data Mining, Databases
• Tools and systems: Data Visualisation, Big Data Processing, High-Performance Computing
• Experiential learning: Labs, internships, undergraduate research, capstone project

Entry requirements

Admission to the undergraduate programme is based on academic preparation in STEM subjects. Applicants are expected to demonstrate strong achievement in mathematics (including calculus), science and computer science where available. Admissions typically consider secondary-school grades, the strength of the curriculum, letters of recommendation and any relevant extracurricular activities such as computing or research projects.

Prospective students whose first language is not English will need to meet the university's English language proficiency requirements. Transfer applicants should have completed relevant college-level coursework in mathematics and programming. As this is a competitive STEM programme, successful applicants usually present a solid foundation in analytical and computational subjects.

Career prospects

Graduates are prepared for roles across industry, government and research where data-driven decision-making is required. Typical job titles include data scientist, data analyst, machine learning engineer, business intelligence analyst, data engineer and software developer. Alumni work in sectors such as technology, finance, healthcare, manufacturing, energy, and environmental science.

The programme’s emphasis on practical projects, internships and team-based problem solving helps graduates transition into professional roles or continue to graduate study in data science, computer science, statistics, or applied disciplines. Students may also pursue careers that combine domain expertise with data skills, such as computational research positions in engineering and the natural sciences.

Why study at Michigan Technological University

Michigan Technological University offers a hands-on, engineering-focused environment with strong connections to industry and research in computational science and data-intensive engineering. Students benefit from project-based learning, access to computing and high-performance resources, and opportunities for undergraduate research alongside faculty in data science, computer science and engineering disciplines.

The university emphasises experiential learning through internships, co-operative education opportunities, and a capstone experience that often involves collaboration with industry or government partners. Small class sizes and a supportive community help students gain personalised mentorship, while regional and national employer connections support internship and job placement. The campus environment also fosters interdisciplinary work, allowing students to apply data science skills to real problems in engineering, natural resources and the applied sciences.