Program Overview

MSci Data Science - 2026 entry

Overview

This course has been developed in collaboration with industry, using current methods, platforms, software and data, to ensure you are fully prepared for workplace practice upon graduation. Throughout your studies at Exeter, you will develop fundamental mathematical and computational techniques via a mixture of individual and group learning. Our programmes support you in becoming an outstanding, dynamic problem solver with an excellent technical skillset, preparing you for a fantastic array of professions that require the technical expertise of a data scientist. Taught by active researchers who are experts in their fields, covering the core areas of mathematics and computer science while introducing you to applied data science as well as social context. Research projects in each academic year will allow you to develop research and project management skills in an area of interest, using real world datasets and guided by a leading academic supervisor. Pursue your studies to Masters level in your final year with the freedom to choose advanced modules to suit your interests.

Entry requirements (typical offer)

• Qualification: A-Level
• Typical offer: AAA-AAB
• Required subjects: GCE A-Level Maths grade B in Mathematics, Pure Mathematics or Further Mathematics
• Qualification: IB
• Typical offer: 36/666-34/665
• Required subjects: HL 5 in Mathematics (Analysis and approaches or Applications and interpretations)
• Qualification: BTEC
• Typical offer: DDD
• Required subjects: Applicants studying a BTEC Extended Diploma are also required to achieve a grade B at A-Level in Mathematics
• Qualification: GCSE
• Typical offer: 4/C
• Required subjects: Grade 4/C in GCSE English Language
• Qualification: Access to HE
• Typical offer: 30 L3 credits at Distinction Grade and 15 L3 credits at Merit Grade
• Required subjects: 12 L3 credits at Merit Grade in an acceptable Mathematics subject area
• Qualification: T-Level
• Typical offer: T-Levels not accepted
• Required subjects: N/A

Contextual Offer

• A-Level: ABB-BBB
• IB: 32/655-30/555
• BTEC: DDM
• Specific subject requirements must still be achieved where stated above. Find out more about contextual offers.

Other accepted qualifications

View other accepted qualifications

English language requirements

International students need to show they have the required level of English language to study this course. The required test scores for this course fall under Profile B1. Please visit our English language requirements page to view the required test scores and equivalencies from your country.

Course content

Year 1

• Compulsory modules:
  • ECM1400: Programming (15 credits)
  • ECM1410: Object-Oriented Programming (15 credits)
  • COM1011: Fundamentals of Machine Learning (15 credits)
• Optional modules:
  • ECM1407: Social and Professional Issues of the Information Age (15 credits)
  • ECM1413: Computers and the Internet (15 credits)
  • ECM1414: Data Structures and Algorithms (15 credits)
  • ECM1415: Discrete Mathematics for Computer Science (15 credits)
  • ECM1416: Computational Mathematics (15 credits)

Year 2

• Compulsory modules:
  • ECM2414: Software Development (15 credits)
  • ECM2419: Database Theory and Design (15 credits)
  • MTH2006: Statistical Modelling and Inference (30 credits)
  • COM2011: Machine Learning and Data Science (15 credits)
  • SPA2009: Data Science in Society (15 credits)
• Optional modules:
  • COM2020: Team Project (15 credits)
  • Select 15 credits from:
    • COM2014: Computational Intelligence (15 credits)
    • Free choice elective (15 credits)
    • ECM2423: Artificial Intelligence and Applications (15 credits)
    • ECM2427: Outside the box: Computer Science Research and Applications (15 credits)

Year 3

• Compulsory modules:
  • COM3021: Data Science at Scale (15 credits)
  • ECM3401: Individual Literature Review and Project (45 credits)
  • COM3031: Probabilistic Machine Learning (15 credits)
• Optional modules:
  • Select up to 45 credits:
    • ECM3408: Enterprise Computing (15 credits)
    • ECM3412: Nature Inspired Computation (15 credits)
    • ECM3422: Computability and Complexity (15 credits)
    • ECM3428: Algorithms that Changed the World (15 credits)
    • ECM3446: High Performance Computing (15 credits)
    • COM3024: Computer Vision (15 credits)
    • COM3029: Social Networks and Text Analysis (15 credits)
    • MTH3019: Mathematics: History and Culture (15 credits)
    • MTH3024: Stochastic Processes (15 credits)
    • MTH3028: Statistical Inference: Theory and Practice (15 credits)
    • MTH3041: Bayesian statistics, Philosophy and Practice (15 credits)
  • You may select up to 30 credits of other options:
    • EMP3001: Commercial and Industrial Experience (15 credits)
    • Free choice elective - Up to 30 credits (30 credits)

Final year

• Compulsory modules:
  • ECMM427: Group Development Project (30 credits)
  • ECMM428: Individual Research Project (30 credits)
• Optional modules:
  • Select 4 of the following:
    • ECMM422: Machine Learning (15 credits)
    • ECMM423: Evolutionary Computation & Optimisation (15 credits)
    • ECMM424: Computer Modelling and Simulation (15 credits)
    • ECMM426: Computer Vision (15 credits)
    • MTHM047: Bayesian Statistics, Philosophy and Practice (15 credits)
    • ECMM447: Social Networks and Text Analysis (15 credits)
    • MTHM033: Statistical Modelling in Space and Time (15 credits)
    • COMM113: Deep Learning (15 credits)
    • COMM116: Generative AI Applications (15 credits)
    • COMM117: Large Language Models and Applications (15 credits)
    • COMM118: AI in Healthcare (15 credits)
    • COMM119: AI in Environment (15 credits)
    • COMM039: Network Science (15 credits)
    • COMM040: Text Mining and Natural Language Processing (15 credits)
    • MTHM506: Statistical Data Modelling (15 credits)
    • ECMM464: Security Assessment and Validation (15 credits)
    • ECMM463: Building Secure and Trustworthy Systems (15 credits)

Fees

• UK students: £9,535 per year
• International students: £29,800 per year

Scholarships

The University of Exeter has many different scholarships available to support your education, including £5 million in scholarships for international students applying to study with us in the 2025/26 academic year, such as our Exeter Excellence Scholarships.

Financial support is also available for students from disadvantaged backgrounds, lower income households and other under-represented groups to help them access, succeed and progress through higher education.

Learning and teaching

• Lectures, seminars and workshops
• Virtual learning environment
• A research and practice led culture
• Assessment

Your future

There is an established strong market demand for suitably skilled data scientists and data science skills are increasingly being sought across the sectors, particularly by the finance and accounting industries, supermarkets, online retailers such as Amazon, and the NHS.

This Data Science course has been developed with partner employers, including IBM, the Met Office, South West Water, Black Swan and Oxygen House and has been designed to deliver skills that are most valued by employers. Modules will use the employers’ methods, platforms, software and data, to ensure that they are fully reflective of workplace practice. Throughout your studies you will conduct individual and group projects using real world data sets.

This course will prepare you to be an outstanding dynamic problem solver with an excellent technical skillset. In addition to learning the core principles of Mathematics and Computer Science, you will learn soft skills that employers have told us they are looking for, such as communication and presentation skills, and the ability to work effectively in a team.

The inclusion of individual- and group-based project work in every academic year will offer you an opportunity to apply your skills to solve real world problems and prepare you for future employment.

Industrial Experience

As part of the four-year degree, you can choose to take an optional Commercial and Industrial Experience module during the vacation before the third year (subject to availability). This very rewarding opportunity allows you to gain paid work experience while earning credits towards your degree programme. Following the placement you can report on your experience which, alongside a report from the employer, enables you to count your experience as a third-year optional module. We have excellent links with employers and can provide assistance in finding suitable employment.

Career Paths

The broad-based skills acquired during your degree will give you an excellent grounding for a wide variety of careers, not only those related to Data Science but also in wider fields. Examples of roles recent graduates are now working as include:

• Analytics Manager
• Business Intelligence
• Analyst
• Business Statistician
• Data Analyst
• Data Architect
• Data Scientist
• Machine Learning Engineer
• Quantitative Researcher
• Research Analyst
• Research Scientist