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  • Prepare for the information challenges of the future by becoming proficient in the latest modelling and analysis tools
  • Learn how to use data to provide critical business insights, helping organisations improve their performance and make key decisions
  • Earn while you learn – you are employed full-time by your sponsor company with paid time off to study
  • Pay no tuition fees – these are met by your employer and the Government
  • All learning is relevant to your specific workplace with support from industry professionals
  • Obtain a full Honours degree and benefit from a guaranteed job at the end of the programme

If you have aspirations of working in the Data Science or business analytics field, within any organisation, you no longer have to choose between a job and university study – you can now do both.

Our highly stimulating Data Science Degree Apprenticeship programme provides the opportunity to develop your knowledge, understanding, technical skills and confidence to operate successfully in a globalised professional environment, where working in multidisciplinary teams alongside domain experts is often the norm.

A Degree Apprenticeship allows you to study on a day-release or block basis while also earning a salary, enabling you to gain the necessary qualifications and experience to progress. The University of Winchester has worked closely with the Institute for Apprenticeships and Technical Education as well as business partners to ensure that work-based training and rigorous academic study are blended effectively to meet your own needs and those of your employers.

Data science is an interdisciplinary field of methods, processes, algorithms and systems to extract insights from data. Finding information in diverse datasets to address complex problems and improve organisational processes is key to the Data Scientist role. This degree apprenticeship is built on activities which develop the different aspects of data science and leads to an Honours degree qualification that is contextualised for workplace competency as a data science professional capable of making a real contribution to your employer. 

The modern business world increasingly requires a higher level of data science expertise as well as abilities in problem solving and data analytics. As a data scientist you can have an impact at strategic and operational levels by building and maintaining collaborative relationships with key stakeholders, subject experts and colleagues as well as the wider data science community. You will explore innovative ways to use a range of techniques to find significant and valuable patterns in data, transforming these into information for your organisation.


As a qualified degree apprentice, you will be an agile and skilled data scientist capable of rising to the challenges of the workplace. You will be prepared for a wide variety of professional roles in the field including data scientist, business intelligence analyst, IT consultant, machine learning practitioner or IT project manager.

Pre-approved for a Masters

If you study a Bachelor Honours degree with us, you will be pre-approved to start a Masters degree at Winchester. To be eligible, you will need to apply by the end of March in the final year of your degree and meet the entry requirements of your chosen Masters degree.


Suitable for applicants from:


Learning and Teaching

The University aims to shape 'confident learners' by enabling students to develop the skills to excel in their studies here and be transferable to further studies or the employment market. Staff and students form a community of learners who, together and independently, seek to generate and exchange knowledge. Over the duration of the course, students develop independent and critical learning, building confidence and expertise progressively through independent and collaborative research, problem solving, and analysis with the support of staff. Students take responsibility for their own learning and are encouraged to make use of the wide range of available learning resources available.

In addition to the formally scheduled contact time (i.e. lectures, seminars etc), students are encouraged to access academic support from staff within the course team, personal tutors and the wide range of services to students within the University.

This programme is centred on a real job within a business that extends the learning beyond the classroom and into the workplace. The aim is to integrate academic learning at degree level and on-the-job practical training to provide a holistic programme of education and training to meet the skills needs of employers now and in the future. This programme uses a variety learning strategies and delivery methods that develop academic and practical skills, encourage critical reflection and provide support to all students. The diverse nature of the methods used help to enhance student employability and professional development.

The exact nature of your learning experience will depend on which employer you choose to apply to - the key skills you will learn are suited to those looking to begin or develop a career in data science.


The University is committed to providing timely and appropriate feedback to students on their academic progress and achievement, enabling them to reflect on their progress and plan their academic and skills development effectively. Students are also encouraged to seek additional feedback from their course tutors and lecturers.

At the University of Winchester validated programmes may adopt a range of means of assessing your learning. An indicative, and not necessarily comprehensive, list of assessment types you might encounter includes essays, portfolios, supervised independent work, presentations, written exams, or practical performances. The University is committed to ensuring that all students have an equal opportunity to achieve module learning outcomes. As such, where appropriate and necessary, students with recognised disabilities may have alternative assignments set that continue to test how successfully they have met the module's learning outcomes. Further details on assessment types used in the programme you are interested in can be found on the course page, by attending an Open Day/Evening, or contacting our teaching staff.


Taught elements of the course take place on campus in Winchester.

Entry Requirements

Students must be employed by a sponsoring business. Programme-specific entry requirements apply, as agreed with the sponsoring organisation.

Candidates for the Degree Apprenticeship should normally have a minimum of 104-112 points* at A2 (Grades BCC) or an equivalent e.g. BTEC DMM. You will also require Maths and English Language GCSE grade A*- C. We will individually evaluate candidates who do not meet these requirements, but have workplace experience or recognised prior learning.

*Note: Different employers will identify their specific entry requirements

To secure an apprenticeship you will need to apply for a full-time position with one of our partner employers.  To gain a place on this programme you will have been successful on application and interview as conducted by the sponsoring business in conjunction with the University of Winchester.

Alternatively, you may be seeking career enhancement within your current role. In this case you will need your employer to support you by offering a Degree Apprenticeship with the University of Winchester.

In the absence of formal learning qualifications applications are welcomed from persons who can demonstrate relevant work experience, including work in a voluntary capacity. The course structure actively supports claims for Accreditation of Prior Certificated Learning (APCL) and Accreditation of Prior Experiential Learning (APEL).

Selection process:

Candidates are invited to attend an interview or assessment centre organised by the business in conjunction with the University of Winchester.

If English is not your first language: Year 1/Level 4: IELTS 6.0 (including 6.0 in writing) or equivalent

Course enquiries and applications

Please contact Stella McKnight:

Telephone: +44 (0) 1962 826478
Or Send us a message

Visit us

Explore our campus and find out more about studying at Winchester by coming to one of our Open Days.

Year 1 (Level 4)

Modules Credits

Work Based Studies - Organisational Functions in Context 15

This module is designed to allow flexibility of study, to enable employed students to gain credit for work based activities, and to contribute to the continued development of academic and professional skills. The module aims to develop student understanding of the relationships and dependencies that exist between different functions within an organisation, and their contribution to the overall performance of the business, with particular reference to the key functions within the organisation in which they are employed. 

Students will work individually, in groups and with their workplace mentor to develop the knowledge and skills required to analyse, and present, ideas and coherent arguments both orally and in writing. Students will build on their communication, negotiation and reflective practice skills, to support their on-going personal, academic and professional development.

Work Based Studies - Personal and Professional Development 30

This module is designed to allow flexibility of study, to enable employed students to gain credit for work-based activities, and to contribute to the continued development of academic and professional skills. The aim of this work-based learning module is to help students develop a deeper understanding of how their organisation operates including examining the relationships and dependencies that exist between different functions within an organisation, and to help them to recognise how their own contribution can form the foundations of a successful career within the company. To achieve this, students will reflect on themselves, their skills, and how they fit within their company. With support from a work-based mentor, students will identify their development needs, manage their own development, and reflect critically on their learning. This module provides the foundation for continuing personal and professional development building confidence to provide students with the potential to maximise both academic and career aspirations.

Introduction to Quantitative Data Analysis 15

This module introduces students to quantitative ideas and procedures which are fundamental to the work of the professional data scientist. It will take a real-world approach to understanding the value and meaning of data and collecting and preparing data for processing. Students will be introduced to basic statistical concepts and analytical software tools for specific data analysis tasks. This will provide students with a practical knowledge of applied computing which will help to underpin more advanced study found later in the programme modules. It will also support longer term academic and professional development. The module enables students to explore analytical methods and appreciate critically the professional literature and research in the field of data analysis.

Digital Business and Emerging Technologies 15

The emergence of the digital economy has unlocked new opportunities for businesses, whilst creating new modes of competition in both traditional and new sectors of the economy.  The aim of this module is to impart an understanding of digital business together with the practices and processes required to develop effective digital strategies.  Identifying multiple technologies for integration into business, juxtaposed with the development of new digital strategies is central to corporate success, however, this is often a complex task.  This module provides insight into the emergence of digital business, key concepts, technologies, and strategic organisation to develop a multidisciplinary appreciation of how new technologies can directly shape businesses and processes.

Introduction to Software Development 15

This module will introduce technical students to programming and testing concepts. Students will develop an understanding of the general principles of how programs and projects are delivered within the IT services industry and the application of the concepts of testing and programming. The module will develop introductory skills using a specific but not defined programming language and platform. Undertaking the module will develop the student’s skills in developing and testing programmes using industry techniques.

Data Structures and Operating Systems 15

This module imparts an understanding of operating systems including concepts such as scheduling, concurrency and synchronisation, memory management, input and output systems, kernel security and file systems.  Fundamentals of data structures and core algorithms and analysis are also explored.


Database Analysis and Design 15

This module imparts an understanding of analysis and design concepts that are essential for developing and implementing software and database systems.  Design concepts and procedures such as Business Rules, Requirements Analysis, Data Modelling, Relational Data Modelling, Object Orientated Analysis and SQL will be explored.  Students will also learn how to apply Unified Modelling Language (UML) within different computing scenarios.

Introduction to Probability and Statistics 15

This module introduces probability and distribution theory and then shows how these allow statistical inference from data. The first part of the module defines probability via axioms and introduces random variables and distributions of practical importance, including multivariate distributions. The second part then shows how data can be modelled as random variables, leading to basic methods of frequentist or, in principle, Bayesian inference. The software R is used to explore properties of distributions and to make basic statistical inferences from data.

Year 2 (Level 5)

Modules Credits

Artificial Intelligence 15

This module introduces the field of artificial intelligence and the fundamental concepts and techniques in the areas of problem solving, knowledge representation and machine learning.  Agents, Search, Planning, Knowledge Representation and Bayesian Artificial Intelligence are explored.

Object Orientated Software Development 15

The module develops theoretical and practical skills in software engineering principles including abstraction, encapsulation, aggregation and inheritance.  Students will learn the processes from analysis to design, implementation, testing and documentation together with software quality principles. 

Visualisation of Business Intelligence 15

This module introduces the activities of preparing data for presentation through cleansing and validation processes.  It introduces students to presentational techniques for visualisation as a form of recording, understanding and communication of transformed data. Visualisation techniques are important because they can present large and overwhelming amounts of multi-source and multi-format data. The business need is for data to be presented in perceptible, comprehensible, relevant and usable visual forms to communicate complex ideas that support decision making. The module will cover the presentation of data using industry standard techniques, and the advantages and limitations of a wide range of visualisation approaches such as basic statistical charts through to more complex formats. The choice and selection of a range of visual formats is considered and practised using examples related to the business environment. Students will have the opportunity to develop skills with visualisation tools used in the workplace.

Predictive Data Analytics 15

In this module you will develop professional skills in applying data analysis techniques and processes involved in conducting projects within the business environment.  It will introduce some of the most widely used predictive modelling techniques and their core principles.  You will form a solid foundation of predictive data analytics using relevant tools and technology for building statistical or machine learning models to make predictions based on data.  You will learn how to carry out exploratory data analysis to gain insights and prepare data for predictive modelling, an essential skill valued in business.  This module will also help understand and unlock the power of large datasets, acquiring some practical skills in data science to create data visualisations and carry out analyses.

Work Based Studies – Applied Project and Consultancy Management 15

This module is designed to allow flexibility of study, to enable employed students to gain credit for work based activities, and to contribute to the continued development of academic and professional skills.  The aim of this work-based learning module is to build on the student’s existing knowledge of Project and Consultancy Management and Project Management documentation. Through undertaking research into their own organisation students will develop both a theoretical and practical understanding of project and consultancy management including; the importance of effective liaison with delivery project managers and management of client expectations. In completing this module students will draw upon learning from other modules, and reflect on how this module can support the students’ professional development and enhance their performance as effective practitioners. The module will encourage students to demonstrate the use of different management and communication styles within a team and project environment.  Students will build upon previous personal and interpersonal skills and assess the impact of their own communication styles and team member’s styles on team dynamics. Students will reflect on the challenges of managing a project for themselves with the view to developing students’ awareness of application in the contemporary business world.

Work Based Studies – Computer Systems and Network Management 15

This module will explore both technical and client management.  Students will explore how to plan, design and manage computer networks with an overall focus on the services and capabilities that network infrastructure solutions enable in an organisational context including; identification of network security risks and their resolution.  Students will also understand concepts of technology and client management and recognise any inter-relationships between them. The module is also aimed at helping students to develop some of the skills involved in managing and leading people, thereby further cultivating the self-awareness that characterises outstanding managers and leaders.

Statistical Methods 30

The first half of this course builds on the material covered in probability and statistics by investigating several aspects of statistical distribution theory. The aim of the second half of this module is to describe the theory and methods of using linear statistical models in analysing data to understand the influence of one or more explanatory variables and to make predictions about the response.

Year 3 (Level 6)

Modules Credits

Cloud Computing and Infrastructure 15

Cloud computing describes a new supplement, consumption, and delivery model for IT services based on the Internet. Cloud computing is a consequence of the ease-of-access to remote computing sites provided by the Internet. Within this context, the aim of this module is to develop student understanding of cloud technologies, infrastructure and deployment. The values that cloud computing may bring to an organisation will be evaluated. An array of cloud products and services will be appraised in the context of how they may apply to different types of organisation and their operational functions. Students will appraise an array of methods used to provide virtual storage and network virtualisation. The security and ethical challenges inherent in an organisational transformation to cloud computing will be evaluated.

Strategic Forecasting and Simulation 15

This module covers the two major data driven business prediction topics of forecasting and simulation.  Forecasting is the analysis of trends in data and is a subject related to probability, risk and uncertainty. The module covers the main numerical forecasting methods and their accuracy limitations.  Less formal judgemental methods are also covered. Simulation, in a business context, is about constructing data driven models to emulate real world systems with sufficient fidelity and validity, so that the possible impacts caused by changes to its component variables can be explored experimentally. The module will cover the principles of simulation and simulation model building. Students will have the opportunity to develop advanced spreadsheet modelling and problem structuring methods.

Strategic Analytics 15

This module provides students with a deeper understanding of how data is used by strategic decision makers. Students will study the concepts of ‘Big Data’ and data storage. The current strategic issues of concern to the data scientist will be considered. Students will also examine the analysis and storage issues for unstructured data. The module will conclude with a data analytics case study where the student will be required to work through the life cycle of the data analytics case study using appropriate techniques and methods, reporting on the findings and making critical recommendations to a given strategic stakeholder.

Mathematical Optimisation 15

This module is primarily concerned with the fundamentals of Mathematical Optimisation and practical aspects of numerical solutions arising from real-world problems. The course will focus on:

  • Convex sets, functions, optimisation problems.
  • Basics of convex analysis. Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems.
  • Optimality conditions, duality theory, theorems of alternative, and applications. Interior-point methods.
  • Applications to statistics and finance.
Data Science Project (Apprenticeship) 30

This Data Science Project demonstrates the application of relevant professional skills and behaviours to meet the outcomes in accordance with the Data Scientist (integrated degree) standard.  Evidence of appropriate project planning and research methodologies will be shown together with an evaluation of the processes followed and recommendations for future activities.

Data Scientist Portfolio 30

The Data Scientist Portfolio will provide substantive evidence from real-work projects and enable the apprentice to demonstrate how they have applied data science in a real-work environment. It is also used to help the apprentice to answer questions in the Professional Discussion. It will showcase elements of work that describe the apprentice’s competency against knowledge, skills and behaviours identified by the Institute for Apprenticeships and Technical Education under their Data Scientist (integrated degree) standard.

The real-work projects are typically undertaken alongside the apprentice’s normal duties with their employer. This assessment has two elements: Knowledge Test and Professional Discussion. The latter is informed by a portfolio, which is delivered in the form of an ePortfolio with a presentational element.

Issues with regard to confidentiality and/or security will be addressed between the University, employer and apprentice allowing the data scientist portfolio to have business value from being undertaken using real-work data.

Please note the modules listed are correct at the time of publishing, for full-time students entering the programme in Year 1. Optional modules are listed where applicable. Please note the University cannot guarantee the availability of all modules listed and modules may be subject to change. For further information please refer to the terms and conditions at
The University will notify applicants of any changes made to the core modules listed above.

Progression from one level of the programme to the next is subject to meeting the University’s academic regulations.

2023 Course Tuition Fees 

Costs to Student/Apprentice

  • Apprentices are full-time employees and will be entitled to a wage and paid time off to study
  • There are no tuition fees

Read What our Degree Apprenticeships? for more information.

Employer Information

How much will it cost an employing business per apprentice?

The apprentice will be an employee who will work for a business throughout the programme - a market-rate salary is anticipated. The funding you are eligible for is determined by the number of people you employ, the annual payroll of your company and the age of the learner.

Read Information for employers for the latest information.


As one of our students all of your teaching and assessments are included in your tuition fees, including, lectures/guest lectures and tutorials, seminars, laboratory sessions and specialist teaching facilities. You will also have access to a wide range of student support and IT services. 

There might be additional costs you may encounter whilst studying. The following highlights the mandatory and optional costs for this course:


Core Texts

Multiple copies of core text are held within the library and e-books are identified where possible, however due to limited availability students are recommended to purchase a copy for their own use. It is possible for students to purchase second-hand copies. Indicative cost: £50 - £500 per year.


Printing and Binding

Students are required to pay for the costs of dissertation printing and binding (if applicable). Indicative cost: £10.


Key course details

3 - 4 years part-time
Typical offer
104-112 points (see Entry Requirements for more information)
On campus, Winchester