Northern Arizona University

What you'll study

The Data Science bachelor programme blends mathematics, computer science and applied statistics to prepare you for working with large, complex data sets. Core coursework typically covers calculus and linear algebra, probability and mathematical statistics, data structures and algorithms, and introductory and advanced programming (commonly Python and/or R).

• Foundations: Calculus, Linear Algebra, Discrete Mathematics, Introduction to Programming.
• Statistics and Modelling: Probability, Statistical Inference, Regression Methods, Time Series Analysis.
• Computational Data Science: Data Structures & Algorithms, Scientific Computing, Numerical Methods, Parallel Computing.
• Data Engineering: Databases, Data Wrangling, Data Management, Big Data Technologies and Cloud Computing concepts.
• Machine Learning & AI: Supervised and Unsupervised Learning, Model Evaluation, Deep Learning fundamentals.
• Applications and Visualisation: Data Visualisation, Geospatial Data Analysis, Domain-specific electives (e.g. environmental data, bioinformatics, business analytics).
• Professional Experience: A capstone project or practicum in which students complete a semester-long, team-based project with real data; opportunities for internships and undergraduate research are strongly encouraged.
• Ethics and Communication: Courses or modules on data ethics, privacy, reproducible research and communicating insights to non-technical audiences.

The programme typically runs over four years, incorporating general education requirements alongside major coursework and elective options to allow interdisciplinary study and specialisation.

Entry requirements

Applicants are expected to hold a recognised high-school diploma or equivalent. Admissions consider the overall academic record; successful applicants normally demonstrate strength in mathematics (including algebra and precalculus or calculus) and science. Prior exposure to programming is advantageous but not always mandatory.

• High-school transcript with a solid academic record, particularly in mathematics and quantitative subjects.
• Evidence of college-preparatory coursework (calculus or precalculus, and courses demonstrating analytical skills).
• Application materials such as a personal statement or essays, and letters of recommendation may be requested or strengthen an application.
• Transfer applicants should provide college transcripts and details of completed prerequisite coursework; AP/IB credits may be evaluated for placement.
• Applicants whose first language is not English will need to satisfy the university's English proficiency requirements.

Specific admission criteria and any testing requirements vary; consult Northern Arizona University's admissions resources for the definitive list of requirements for first-year and transfer students.

Career prospects

Graduates of the Data Science programme move into roles across industry, government and academia where they apply computational and statistical techniques to extract value from data. Common career paths include:

• Data Analyst and Business Intelligence Analyst — analysing datasets to support decision making and reporting.
• Data Scientist and Machine Learning Engineer — developing predictive models, machine learning pipelines and production systems.
• Data Engineer — designing and maintaining data infrastructure, ETL pipelines and databases.
• Research Assistant or Analyst in scientific and environmental fields — applying data methods to research projects, especially where spatial and temporal data are important.
• Software Developer or Quantitative Analyst — roles that combine programming skills with analytical thinking.
• Further study — many graduates go on to specialised master’s or doctoral programmes in data science, statistics, computer science or domain-specific disciplines.

Internships, capstone projects and collaborations with faculty give students practical experience valued by employers across sectors such as technology, healthcare, finance, government, and environmental organisations.

Why study at Northern Arizona University

Northern Arizona University offers a hands-on, interdisciplinary approach to data science education with opportunities for close faculty mentorship and collaborative projects. The programme emphasises experiential learning through labs, undergraduate research and externally focused capstone projects.

• Access to computing resources and laboratory facilities that support data analysis, visualization and high-performance computing coursework.
• Opportunities to work on applied data problems tied to the region — for example, environmental and geospatial datasets — leveraging NAU's location and research strengths.
• Collaborative environment with opportunities to take electives or minors in related departments such as Computer Science, Statistics, Geospatial Sciences and Engineering.
• Career support services and employer links that help students secure internships and transition into professional roles.

Together, these features equip graduates with the quantitative, computational and communication skills needed to succeed in a rapidly evolving data-driven job market.