Norwich University

What you'll study

The programme builds core skills in statistics, programming, data management and visualisation, and emphasises practical application through labs, projects and an integrative capstone. Learning is organised to develop both technical competence and the ability to translate data insights for stakeholders.

• Foundations: introductory and intermediate statistics, linear algebra essentials for analytics, and computational thinking.
• Programming & software: instruction in languages commonly used for analytics (such as Python and SQL), plus experience with data-analysis libraries, scripting, and version control.
• Data management: database design and querying, data warehousing concepts, and best practices for data cleaning and preprocessing.
• Modelling & machine learning: regression, classification, clustering and basic predictive modelling techniques, with an emphasis on interpreting model results and validation.
• Data visualisation & communication: visual analytics, dashboard design and storytelling with data to support evidence-based decisions.
• Applied electives: domain-focused applications such as business analytics, healthcare analytics, geospatial data, or cyber/defence analytics to reflect Norwich University’s broader strengths.
• Capstone & experiential learning: a culminating team project that applies methods to a real dataset or client brief; opportunities for internships or practicum placements provide workplace experience.
• Professional skills: coursework and workshops on ethics in data use, privacy, reproducibility, and communicating with non-technical audiences.

Entry requirements

Admission is typically based on a completed secondary-school qualification or recognised equivalent. Successful applicants usually demonstrate proficiency in mathematics and analytical reasoning. Specific expectations include:

• A high school diploma or equivalent with evidence of academic achievement.
• Recommended preparatory subjects: algebra/trigonometry, calculus (where available), and any prior computing or programming coursework.
• Evidence of quantitative aptitude—this can include school transcripts, standardised test scores where required by the institution, or examples of prior analytic work or coding projects.
• For applicants whose first language is not English, proof of English language proficiency through recognised tests, unless exempted by previous study in English.
• Where relevant, a resume or statement of purpose outlining interest in data analytics, and references can strengthen an application. Norwich University may also consider extracurricular achievements and leadership experience.

Career prospects

Graduates are prepared for entry-level roles that require turning data into actionable insight across public and private sectors. Typical job titles and pathways include:

• Data Analyst or Business Intelligence Analyst — analysing operational or commercial data and producing reports and dashboards.
• Junior Data Scientist or Predictive-analytics Associate — developing and validating statistical models under senior supervision.
• Data Engineer (entry-level) — supporting data pipelines, ETL processes and database management.
• Analytics roles in industry sectors such as finance, healthcare, manufacturing, defence and government, where Norwich’s connections can be beneficial.
• Progression to specialised technical roles or postgraduate study in data science, statistics, computer science, or business analytics.

Why study at Norwich University

Norwich University combines a strong undergraduate liberal-arts tradition with applied, career-focused education. The Data Analytics programme emphasises hands-on learning through labs, projects and internships, with relatively small class sizes that allow close interaction with faculty. Students benefit from practical capstone experiences and opportunities to work on problems linked to local employers, government or military-affiliated organisations. The curriculum balances technical training with ethical and communication skills so graduates can responsibly apply data-driven approaches in a range of professional settings.