University of Leicester

What you'll study

This programme covers the technical foundations of artificial intelligence and machine learning together with applied business intelligence, data engineering and data visualisation. Teaching emphasises hands-on practice with real-world datasets, using industry-standard tools and cloud resources, and culminates in a substantial project or dissertation solving a business problem.

• Core technical topics: supervised and unsupervised machine learning, deep learning foundations, probabilistic models, model evaluation and deployment.
• Data engineering and management: databases and data warehousing, ETL and data pipelines, scalable data processing (batch and stream), data governance and quality.
• Business intelligence and analytics: business analytics, decision support systems, KPI design, performance measurement and translating analytics into actionable insight.
• Visualisation and communication: interactive dashboards, information visualisation principles, storytelling with data and communicating results to stakeholders.
• Ethics, privacy and regulation: ethical AI, fairness and bias mitigation, data protection and responsible use of analytics in business settings.
• Project/dissertation: an independent applied research or consultancy-style project with a business or organisational focus, integrating technical implementation, evaluation and business impact assessment.

Teaching methods typically include lectures, practical labs, case studies, group coursework and a supervised project. Assessment is by a mix of coursework, project reports, presentations and a dissertation or industry-aligned capstone.

Entry requirements

Applicants are normally expected to have a good honours degree (typically a 2:1 or equivalent) in computer science, information technology, mathematics, statistics, engineering, economics or another quantitative discipline. Applicants with a lower honours degree but significant relevant professional experience, or a degree in a non-quantitative field supported by demonstrable programming and quantitative skills, may also be considered.

Typical prerequisites include familiarity with at least one programming language (commonly Python), basic statistics and linear algebra. Applicants whose first language is not English will need to meet the University's English language requirement (evidence of an approved English test at the stipulated level or equivalent). Additional preparatory modules or bridging units may be recommended for applicants without sufficient prior computing or programming experience.

Career prospects

Graduates are prepared for roles where AI and analytics drive business decisions. Common career paths include:

• Data scientist or machine learning engineer, developing models and analytical solutions for commercial problems.
• Business intelligence analyst or analytics consultant, translating data into strategic insights and dashboards.
• Data engineer, building and maintaining data pipelines and infrastructure for analytics teams.
• Product manager or analytics lead in technology, finance, retail, healthcare, consultancy and public sector organisations.

The programme equips students with a portfolio of practical projects and the technical/communication skills employers expect, and can also serve as preparation for further research or doctoral study in AI and data science.

Why study at University of Leicester

The University of Leicester offers a balance of strong technical teaching and an emphasis on applied, industry-relevant skills. Students benefit from access to computing facilities, specialist labs and supervised project opportunities that connect to local and national industry partners. The university’s careers and employability services provide support with placements, CVs, interview preparation and employer networking to help bridge study and professional practice.

Leicester’s academic staff bring research expertise in areas related to artificial intelligence, data science and analytics, ensuring course content reflects current methods and challenges. Cohorts are typically small enough to allow close supervision of projects and personalised feedback, while cross-disciplinary links enable students to explore the business, ethical and regulatory context of AI deployment.