Michigan Technological University

What you'll study

This programme combines a rigorous foundation in mathematics and probability with applied statistical methods and computational tools used in biological and medical research. Core topics include probability theory, mathematical statistics, regression and ANOVA, experimental design, and statistical inference. Coursework places strong emphasis on computational skills, including data management, statistical programming in R and Python, reproducible research, and simulation methods.

Biostatistics-focused modules typically cover survival analysis, longitudinal data analysis, generalized linear models for discrete and count data, categorical data analysis, and Bayesian methods for biomedical applications. Students also study interdisciplinary subjects such as bioinformatics, epidemiology, and laboratory methods for biomedical data to understand the scientific context of the data.

The curriculum normally includes:

• Mathematics and probability sequence (calculus and linear algebra)
• Core statistics: mathematical statistics, regression, ANOVA and experimental design
• Advanced topics: survival analysis, longitudinal models, categorical data analysis, Bayesian statistics
• Computing and data science: statistical programming, data visualisation, machine learning basics, database management
• Domain electives: epidemiology, bioinformatics, genetics, public health methods
• Capstone experience or practicum involving a real dataset or collaborative research project with faculty, industry or healthcare partners
• General education and science electives, often including biological sciences or biomedical engineering courses

Entry requirements

Admission to Michigan Technological University is competitive and applicants for the Statistics (Biostatistics) programme should demonstrate strong preparation in mathematics. Typical successful applicants present high school coursework in calculus and algebra, and they benefit from prior exposure to probability, statistics and computer programming. Performance in mathematics-related subjects and a solid overall academic record are important.

Recommended preparation:

• High school calculus (or equivalent) and strong grades in mathematics
• Background in sciences, particularly biology, is advantageous for the biostatistics emphasis
• Experience or interest in computer programming and working with data
• Personal statement or portfolio that demonstrates quantitative interests and any relevant project, research or work experience

Specific admission thresholds and required documentation are set by the university; applicants should consult Michigan Technological University’s undergraduate admissions guidance for details on academic requirements and application materials.

Career prospects

Graduates with a bachelor’s in Statistics focused on biostatistics enter a range of careers where quantitative analysis of biological and medical data is central. Common roles include biostatistician (entry-level), statistical programmer, data analyst for healthcare or pharmaceutical firms, clinical data manager, and research technician in academic or government laboratories.

Employers include pharmaceutical and biotech companies, contract research organisations, hospitals and health systems, public health agencies, medical device firms, and academic research groups. Many graduates pursue graduate training (master’s or PhD) to qualify for advanced biostatistics or epidemiology roles, or to work as principal investigators and senior analysts. Skills developed in the programme—statistical modelling, programming, data visualisation and study design—also transfer well to broader data science and analytics careers across sectors.

Why study at Michigan Technological University

Michigan Technological University offers a hands-on, computationally focused programme with opportunities for interdisciplinary collaboration across life sciences, biomedical engineering and computer science. Small class sizes and close faculty mentoring allow students to engage directly in research projects, often involving real-world biomedical datasets.

Students benefit from robust computing resources, access to statistical and data-science software, and co‑op or internship connections with industrial partners and regional healthcare organisations. The university’s research culture encourages undergraduate participation in faculty-led projects, capstone collaborations and practicum experiences that build a portfolio of applied biostatistical work.

For students aiming to apply quantitative methods to health and biological problems, Michigan Tech provides a technically rigorous education, strong computational training and practical pathways into industry, public health and further study.