Northern Arizona University

What you'll study

This programme combines foundational mathematics and statistics with computer science, database theory and applied analytics. Coursework emphasises practical, hands-on experience with data cleaning, exploratory analysis, predictive modelling and data visualisation, culminating in a project-based capstone that applies analytic methods to a real dataset.

• Core quantitative skills: statistics, probability, linear algebra and introductory calculus relevant to modelling and inference.
• Programming and software: instruction in programming languages commonly used in analytics (for example Python or R), version control, and use of data analysis libraries.
• Databases and data engineering: relational databases, SQL, data warehousing concepts and basics of data pipelines.
• Machine learning and predictive analytics: supervised and unsupervised learning methods, model evaluation and selection.
• Data visualisation and communication: techniques for presenting results to technical and non-technical audiences, dashboard design and storytelling with data.
• Domain and applied electives: opportunities to apply analytics in domains such as business analytics, health informatics, environmental science, geospatial analysis or social science.
• Capstone or practicum: a project-based course or internship where students solve an applied analytics problem, often in partnership with industry, government or campus research groups.

Typical structure

The degree includes general education requirements, a sequence of data analytics core courses, supporting mathematics and computing courses, and a set of electives for domain focus. Students normally progress from introductory programming and statistics in the first year to advanced analytics and a capstone in the final year. Opportunities exist for undergraduate research, internships and study of applied datasets drawn from the region.

Entry requirements

Applicants should hold a high school diploma or equivalent. Typical expectations include successful completion of college-preparatory mathematics (including algebra and precalculus; calculus is recommended), and coursework in science and English.

• Academic preparation: strong performance in mathematics and quantitative subjects; coursework in computer science or statistics is advantageous.
• GPA and tests: undergraduate admissions consider overall academic record. Standardised test submission policies may vary, so consult the university for current guidance.
• Transfer applicants: transfer students should provide college transcripts; prior coursework in programming, calculus and introductory statistics can reduce time to degree completion.
• Other materials: personal statements, references or a résumé may strengthen an application, particularly for competitive cohorts or scholarship consideration.

Career prospects

Graduates are prepared for roles that require translating data into actionable insight across private, public and non-profit sectors. The programme builds practical skills employers seek and provides a foundation for continued study at graduate level.

• Data Analyst, Business Intelligence Analyst or Reporting Analyst
• Junior Data Scientist or Machine Learning Associate (with further experience or graduate study)
• Data Engineer or Database Analyst (with emphasis on data pipeline and SQL skills)
• Roles in industry sectors such as healthcare analytics, finance, marketing analytics, environmental and geospatial analysis, government data services and technology companies
• Further study options including master's degrees in data science, analytics, computer science, statistics or domain-specific graduate programmes

Why study at Northern Arizona University

Northern Arizona University offers a learning environment with relatively small class sizes, opportunities for experiential learning and access to computing resources and faculty with applied research interests. The university’s location provides links to regional employers and agencies, and students often find opportunities for internships, applied projects and research using local environmental and business datasets. The programme emphasises practical skills, ethical data practice and communication, helping graduates move quickly into applied analytic roles or to continue into postgraduate study.