Data Science Degree Apprenticeship (Level 7)

Duration 24 months
Discipline Computer Science
Programme delivery Part-time blended learning


  • MSc Data Science (Degree Apprenticeship) is an innovative taught course design for professionals wishing to study alongside work.
  • This course is delivered in both Exeter and London.
  • Gain skills in data science and get a formal qualification, while remaining in full time employment.
  • You will cover the fundamental mathematical and computational techniques which underpin data science, machine learning and statistical modelling as well as the wider social context and specific applications of data science.

Programme structure

The modules we outline here provide examples of what you can expect to learn on this degree course based on recent academic teaching. The precise modules available to you in future years may vary depending on staff availability and research interests, new topics of study, timetabling and student demand.

Our MSc Data Science degree apprenticeship is delivered to two parallel cohorts based in Exeter and London.

Apprentices are full-time employees of their organisation, gaining a University of Exeter degree alongside work. The expected commitment is (approximately):

Terms 1 and 2:

  • 4 x 3-day blocks of face to face teaching per term (approximately one per month)
  • Coursework and self-study

Term 3:

  • Individual research projects

Core modules

Introduction to Data Science

Understand the data science revolution and learn about this broad and fast-moving field. Explore the ways in which data science and artificial intelligence are transforming business and society. Develop fundamental skills in programming, data handling, visualisation and statistics, and learn context and vocabulary to support later, more detailed study.

Fundamentals of Data Science

Data science depends on a solid grounding in mathematics and programming. Develop core mathematical and computational skills essential for further study, including linear algebra, probability and common computational tools/ packages. Learn how to process large datasets efficiently using optimisation techniques.

Learning from Data

Use machine learning and statistical modelling methods to effectively derive insight from data. Deal with real data to understand the theory and practice of the principal learning paradigms. Apply machine learning methods such as classification and unsupervised learning, alongside statistical techniques such as regression and clustering. Use, modify and write software to visualise data and help make better decisions.

Data in Business and Society

The social context of data science is essential background for any data scientist. Learn about ethical issues, privacy, governance, legal frameworks and legislation. Consider how complex and powerful technologies such as machine learning, artificial intelligence and big data can be responsibly managed for the benefit of individuals, organisations and society. Learn how to lead multidisciplinary teams to design, create and implement data solutions in a business context.

Machine Learning

Learn about the most prominent supervised and unsupervised machine learning techniques currently applied to, for example, image and speech analysis, medical imaging, and data analysis in science and engineering. Gain a thorough grounding in the theory and application of machine learning, including pattern recognition, classification, categorisation, and concept acquisition. Discover and apply state-of-the-art techniques such as artificial neural networks and transfer learning.

Statistical Modelling

Look in greater detail at the concepts and methods of modern statistics. Learn fundamental concepts in experimental design and classical techniques for statistical inference. Apply a range of statistical tools for point/ interval estimation, hypothesis testing, linear (multiple) regression, generalised linear models and mixed-effects models. Look at the philosophy and practice of Bayesian inference and analysis, and the philosophical comparisons of the latter to classical statistical methods.

Optional Modules (choose two)

Machine Vision

Learn computer vision and image processing techniques to extract meaningful information from the huge volume of images and video content that is now available, such as medical imaging, satellite based remote sensing and social media content. Cover the essential challenges and key algorithms for solving a variety of problems related to the automated processing of visual data.

High Performance Computing and Data Architecture

Learn the skills and knowledge to exploit modern computational resources for data-intensive analysis, high-performance computing and how to manipulate large datasets. Study the diverse range of architectures for storing and processing data, and cover the core principles underlying the design of software and hardware for handling high demand computation.

Information Security

Gain a solid understanding of the vulnerabilities of data collection, storage and communication in modern computer systems, networks and online environments. Explore the foundations of computer security, techniques to secure complex digital systems and gain practical experience in secure management against malicious and criminal exploitation.

Social Networks and Text Analysis

The Web has created complex, relational datasets which are best understood using a network perspective. Much online data is unstructured text, requiring computational methods for analysis of text at scale. Learn the core principles of network science and text analysis using appropriate tools, then apply them to generate insights from complex networks and large text corpora.

Research Project

Research Project A

Apply the knowledge learned in the first year to a significant independent data science project, based on a real-world problem relevant to the student’s business context. Develop project planning, management and implementation skills, as well as experience in independent learning, presentation and writing. Complete an end to end project supported by an academic/industry supervisory team.

Research Project B

Enhance and hone the skills acquired throughout the Masters programme to produce a more advanced data science project focused on the student’s business organisation. Develop a deep understanding of the business requirements and skills needed to produce effective data science projects for genuine applications.

Entry requirements 2019

To enrol, candidates must be employed in a suitable, technical role for the duration of the course.

A good honours degree in a numerate subject from a recognised university. Students are expected to enter this programme with some programming ability.

The Python programming language is used extensively during this course and applicants with experience in other languages will be asked to learn basic Python before commencing the course.

Other qualifications of a similar level can be considered. We would encourage applicants with non-standard qualifications to contact us to discuss their eligibility.

Learning and teaching

Be the first to hear about opportunities to join this programme by registering your interest.

MSc Data Science Degree Apprenticeship is run in partnership with employers in a range of sectors. Occasionally employers will advertise roles that include enrolment on the MSc Data Science Degree Apprenticeship in the offering. For such vacancies applicants will not only be required to successfully apply for a vacancy with one of our partners, but in addition to meet all eligibility criteria and entry requirements for the programme. Vacancies may be advertised at any time. Subscribe to our mailing list and we will let you know of new opportunities as they are made available.

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