Ferris State University

What you'll study

The programme combines foundational general-education courses with a focused major curriculum in data analytics. Core topics include programming for data analysis (commonly Python and R), statistical inference, probability, linear algebra, and database design and SQL. You will study data wrangling, data visualisation and dashboards, machine learning fundamentals, predictive modelling, and data ethics and governance.

• Foundational modules: statistics, probability, calculus or applied mathematics relevant to analytics.
• Programming and systems: introductory and intermediate programming for data, databases and SQL, and an introduction to cloud or distributed computing concepts.
• Analytics and modelling: regression, classification, clustering, time-series analysis, and applied machine learning methods.
• Visualisation and communication: principles and tools for presenting results (dashboards, visual design, storytelling with data).
• Applied experience: project-based coursework, a capstone project or practicum that solves real-world problems, and opportunities for internships with regional employers.

Throughout the programme you will work on team projects, use industry-standard tools and datasets, and learn to communicate findings to both technical and non-technical audiences. Elective choices allow you to emphasize business analytics, data engineering, or additional computer science and business coursework.

Entry requirements

Applicants must meet Ferris State University’s general admission requirements and typically present a high-school diploma or recognised equivalent. Admissions teams look for evidence of quantitative readiness; prior study in mathematics (algebra, precalculus or calculus) is strongly recommended. Background in computer science or experience with programming is advantageous but not always required.

• Standard applicants: completion of secondary education with coursework in mathematics; transcripts demonstrating academic preparedness for university-level study.
• Transfer students: college-level credits in mathematics, statistics or computing may be transferable and can shorten time to degree.
• International applicants: must satisfy Ferris State’s international admission criteria, including English-language proficiency where applicable.

Specific GPA thresholds, prerequisite courses and documentation requirements are set by the university and can vary by applicant type, so check the university’s admissions guidance for precise criteria.

Career prospects

Graduates are prepared for roles that require turning data into insight and actionable recommendations. Common entry-level positions include:

• Data analyst or business intelligence analyst
• Reporting analyst or operations analyst
• Junior data engineer or ETL developer (with elective focus on databases and systems)
• Analytics consultant or market research analyst

Alumni work across sectors such as manufacturing, healthcare, finance, retail, government and technology companies. With additional experience or postgraduate study, graduates can progress to roles in machine learning engineering, data science, analytics leadership or specialised technical roles in data infrastructure. The programme’s emphasis on applied projects and internships helps students build portfolios and employer contacts.

Why study at Ferris State University

Ferris State emphasises hands-on, career-oriented education that connects classroom learning to regional industry needs. The data analytics degree integrates applied projects and practicum opportunities so students gain workplace-relevant experience before graduating. Small-to-medium class sizes promote close interaction with faculty, and career services support internships, résumé development and employer networking.

The university’s location and partnerships with local and regional employers create opportunities for internships and applied research in business, manufacturing, health care and public-sector analytics. Students benefit from access to computing resources and instruction in widely used analytics tools, preparing them for immediate entry into the workforce or continued study in related graduate programmes.