University of Leicester

What you'll study

The programme provides a blend of theoretical foundations and practical skills in data science. Teaching comprises core modules to build a firm grounding, optional modules to tailor your pathway, and a major independent dissertation or project that applies methods to a substantial real-world or research problem.

• Core topics typically include: statistical inference and probability; supervised and unsupervised machine learning; data mining and predictive modelling; computational methods and programming for data science (commonly Python and/or R); databases and data engineering; data visualisation and communication; and research methods for an MSc project.
• Optional and elective topics may include: big data and distributed computing; time series and forecasting; natural language processing; computer vision; optimisation and advanced machine learning; applied Bayesian methods; and domain-specific applications (such as healthcare analytics or finance), depending on availability.
• Project/dissertation: you will undertake an individual research or industry-linked project supervised by academic staff. Projects emphasise end-to-end data science workflows: problem definition, data collection and cleaning, model development, evaluation, and communication of results.
• Modes and structure: the programme is delivered through lectures, practical labs, seminars and project supervision. It is typically available full-time and part-time, with the taught component followed by the dissertation/project period.

Entry requirements

Typical applicants hold a good honours degree in a quantitative, scientific or computing discipline (for example mathematics, statistics, computer science, engineering, physics, economics). Applicants with a different undergraduate background but with demonstrable numeracy, programming ability and relevant work experience may also be considered.

• Applicants are normally expected to demonstrate competence in mathematics or statistics and some programming experience (for instance in Python, R, MATLAB or similar).
• If your first language is not English, you will normally need to meet the university's English language requirements; evidence of suitable English proficiency will be requested as part of the application.
• Applications should include a personal statement outlining your motivation and relevant experience, and academic references. Where appropriate, candidates may be asked to provide examples of prior project work or undertake a short technical assessment.

Career prospects

Graduates from the MSc in Data Science go on to technical and analytic roles across public, private and third-sector organisations. Career paths commonly taken by alumni include:

• Data scientist / machine learning engineer — developing predictive models, building machine learning systems and deploying analytics pipelines.
• Data analyst / business intelligence analyst — turning data into actionable insight for decision-making and reporting.
• Data engineer — designing and maintaining data architectures, ETL processes and scalable data platforms.
• Specialist roles — natural language processing engineer, computer vision engineer, quantitative analyst in finance, or applied analytics roles in healthcare, retail, manufacturing and government.
• Further study and research — progression to PhD study in data science, statistics, artificial intelligence or related fields.

Why study at University of Leicester

The University of Leicester offers data science teaching that draws on strengths in mathematics, statistics and computer science, with staff who are active in research and applied collaborations. You will benefit from an environment that supports practical, hands-on learning through computing labs, access to research supervision for your project, and opportunities to work with real datasets.

• The programme is research-informed and emphasises transferable skills valued by employers: programming, model development, critical evaluation and communication of results.
• Leicester provides central careers and employability services alongside faculty-level support to help with placements, internships and industry contacts.
• Students join a diverse academic community in a city with a growing tech and data sector, offering networking opportunities and exposure to local and national employers.