MSc Applied Data Science (Degree Apprenticeship)

Eligibility Requirements

All applicants must meet the following requirements to be eligible to use funds from the Apprenticeship Levy scheme to enrol in the MSc Applied Data Science (Degree Apprenticeship) programme:

• For apprentices starting the programme on or before 31 December 2025:
  • Aged over 16 years
• For apprentices starting the programme on or after 1 January 2026:
  • Aged 16 to 21, or
  • Under 25 and either a care leaver or have an Education, Health and Care Plan (EHCP) at the start of their apprenticeship.

• Eligible to work in England.
• Spend 50% of their working time in England.
• Not undertaking another apprenticeship or will benefit from Department for Education (DfE) funding during their apprenticeship programme (including student loans).

Commercially funded options are available for employees who do not meet eligibility requirements to use funds from the Apprenticeship Levy scheme. Please contact apprenticeships.digital@buckingham.ac.uk for more information.

Entry Requirements

We welcome applicants from diverse backgrounds who have a passion for data and critical analysis, and wish to upskill or reskill themselves to become a data scientist.

Each eligible applicant is interviewed, and an initial skills assessment is conducted to determine their suitability for a place in the MSc Applied Data Science (Degree Apprenticeship) programme.

The standard entry requirements for the MSc Applied Data Science (Degree Apprenticeship) are:

• • • An Honours degree (2.1 or higher) in a STEM subject OR significant relevant work experience.
    • Must have a good background in mathematics and basic programming skills.
    • Working in a role relevant to data analytics or similar.
    • Achievement of Level 2 Maths and English will need to be evidenced, preferably by the start of the programme, or prior to entering Gateway (i.e. within the 18-month training period)

Applicants who hold a degree from a non-STEM discipline (e.g. finance, business) could be considered for the programme if they have relevant background and skills in mathematics and programming, and are working in a role relevant to data analytics or similar.

Is this programme for you?

We welcome applicants from diverse backgrounds who:

• • Wish to upskill or reskill to become a data scientist.
  • Aspire to progress into the next stage of their career as a data scientist.
  • Aim to gain a formal academic qualification through a rigorous training programme to complement the knowledge and skills gained through their work experience.

The overall programme is structured to provide a solid foundation in relevant knowledge and skills in mathematics, statistics, scripting, and academic study at the onset. The programme then follows the CRISP-DM methodology to train apprentices to conduct data science projects to achieve strategic business objectives. Then apprentices are introduced to a range of software applications and tools that are widely used in industry to conduct complex data science projects. Finally, the apprentices are exposed to leadership styles and strategies, inclusive innovation, business transformation, ethics and regulations through a series of interactive workshops and seminars delivered by industry experts.

All apprentices undertake a substantial ‘Degree Apprenticeship Project’ where they get the opportunity to apply their knowledge and skills to a real-life complex business problem in their workplace.

Programme Delivery Mode

The programme is delivered through a combination of live online sessions and several in-person sessions at the University of Buckingham. Each apprentice is provided with a detailed training schedule showing session times, delivery mode, holidays/breaks and assessment deadlines.

All modules of the MSc programme are delivered on Fridays. One-to-one online academic supervision, one-to-one online learning development coaching sessions, and online quarterly progress reviews are organised at mutually agreed times on weekdays. Apprentices are welcome to attend one-to-one supervision and mentoring sessions in person at The University of Buckingham.

Live online training sessions: The majority of the 18-month training programme is delivered through interactive live online sessions using the Microsoft Teams virtual learning platform. All live online sessions are conducted on Fridays. Recordings of live online sessions are made available for revision purposes.

On-campus training sessions: Between 2-3 in-person training sessions at Buckingham campus are held to offer opportunities for apprentices to network with others from different organisations, and be part of a community of data scientists. All in-person sessions at Buckingham are held on Fridays.

One-to-one supervision: Each apprentice will have an academic supervisor assigned to them towards the latter half of the training period to advice apprentices on their Work-based Dissertation and Apprenticeship Project. These are typically 30-minute online sessions held at mutually convenient times during weekdays. Apprentice’s workplace supervisor attends supervision meetings if and when their presence is needed. Apprentices are welcome to meet with their academic supervisors in person at Buckingham.

One-to-one learning development coaching: Each apprentice will have a learning development coach assigned to them from the onset of the programme to support, monitor and review the achievement of the competencies defined in the L7 DTSS apprenticeship standard. Coaching sessions are typically 30-minute online sessions held at mutually convenient times during weekdays. Apprentices are welcome to meet with their learning development coach in person at Buckingham.

Programme Structure

The programme strikes a balance between theory and practical skills, emphasising technical know-how, innovation and application, to ensure full competency within the workplace.

The programme covers four key areas of training:

1. Academic and research skills: Apprentices from diverse backgrounds will be trained on essential skills necessary for the successful engagement and the completion of the master’s programme. These skills are transferrable enabling apprentices to investigate, identify and evaluate technological strategic solutions for the workplace.
2. Algorithms and techniques for data science: Apprentices will be trained on developing a critical understanding of a wide range of algorithms and advanced techniques, underpinned by an understanding of essential concepts, theories and facts in mathematics, statistics and computing, to design, implement and evaluate data science projects.
3. Methodologies, systems and tools: Apprentices will be trained on a range of methodologies, tools and systems, many at the forefront of data science, to develop practical skills to design, implement, manage and communicate data science projects.
4. Leadership and innovation: Apprentices from diverse backgrounds will be trained on inclusive leadership skills to successfully deliver workplace transformations and build high-performing data teams.

The four areas of training are covered through a well-structured programme consisting of taught modules, a dissertation, workshops and masterclasses, and a substantial individual project to solve a defined business problem.

The overall programme is structured to provide a solid foundation in relevant knowledge and skills in mathematics, statistics, scripting, and academic study at the onset. The programme then follows the CRISP-DM methodology to train learners to conduct data science projects to achieve strategic business objectives. Then apprentices are introduced to a range of software applications and tools that are widely used in industry to conduct complex data science projects. Finally, the apprentices are exposed to leadership styles and strategies, inclusive innovation, business transformation, ethics and regulations through a series of interactive workshops and seminars delivered by industry experts.

The Curriculum

The curriculum of the MSc Applied Data Science (Degree Apprenticeship) is designed to meet the requirements of the Data Analytics Specialist specialism of the L7 DTSS integrated degree apprenticeship standard. The programme consists of six taught modules, a work-based dissertation, a workshop/masterclass based leadership and innovation module, and a substantial individual project to solve a defined business problem.

Research Methods (RMET): This module provides training for postgraduate students on scientific research methodology for collecting data/information and problem-solving skills, irrespective of the methods used and applied in their study. We recognise that apprentices have academic qualifications from a diverse range of education systems. Some apprentices might have decades of relevant professional experience but without any higher education qualification. The RMET module aims to ensure all apprentices have the relevant research and academic skills successfully complete the Level 7 programme.

Mathematics and Statistics for Data Analysis (MSDA): This module teaches the principles and knowledge underlying the basic concepts of numerical computations to solve basic tasks in matrix computation and statistical analyses. We value the understanding of underlying mathematical concepts behind statistical analyses and machine learning algorithms, although we do not expect apprentices on this programme to become mathematicians.

Scripting for Data Analysis (SCDA): This module trains students on the tools and skills of scripting needed to solve problems of preparing and processing data in a variety of formats and situations such as acquiring, cleaning and transforming data, visualizing data patterns and discovering hidden patterns of interest.

Data Exploration and Visualisation (DEAV): This module introduces the principles and approaches of Exploratory Data Analysis (EDA) and effective techniques and methods of visualising data that may in turn reveal information patterns hidden behind the data.
Applied Techniques of Data Mining and Machine Learning (DMML): this module first introduces the fundamental concepts and principles of data mining and machine learning. The module then presents some basic techniques in machine learning and data mining with examples. Based on the basic techniques, the module conducts an in-depth study of advanced state-of-art techniques, and evaluates their strengths and limitations.

Systems and Tools for Data Science (STDS): This module explores existing software tools and platforms for supporting data science. Through a series of case studies and practical workshops, the module shows how different tools (commercial or non-commercial) work and how various data science-related tasks can be performed with the aid of such tools and systems.

Leadership and Innovation in Data Science (LIDS): This module complements the apprentice’s technical capabilities with a set of soft skills and broad awareness enabling them to become inspiring team leaders and senior data scientists within an organisation. The module will focus on cultural awareness, ethics, data governance, enterprise and innovation, inclusive leadership, communication and stakeholder management within the context of applied data science.

Work-based Dissertation (WBDS): This module is designed to demonstrate the apprentice investigating, identifying, and recommending for implementing technological strategic solutions to achieve organisational objectives.

Degree Apprenticeship Project (DAPR): The degree apprenticeship project provides the apprentice an opportunity to obtain an in-depth understanding of a specific area relating to data science, apply knowledge and skills acquired from taught modules and exercise judgement in solving a practical problem of a substantial size and complexity. The project is intended to bridge the gap between the apprentice’s academic knowledge and understanding of data science and real-life problem-solving in order to fulfil the competencies of the apprenticeship.

The curriculum is reviewed annually and kept up-to-date using feedback from apprentices, input from our Industry Advisory Board and external examiners, and staff CPD activities and research.

Further details of the overall programme and individual modules are provided in the Programme Specification and Module Specifications.

Quality teaching

We offer high-quality, small-group teaching, which leads to our degrees being recognised around the world. The standards of degrees and awards are safeguarded by distinguished external examiners – senior academic staff from other universities in the UK – who approve and moderate assessed work. The quality of teaching, learning and assessments are continuously enhanced through peer-observation, feedback, and continuous professional development activities.

High calibre staff

Our academic staff are friendly, approachable, diverse and highly qualified and respected professionals. All academics are research active, many of whom have worked with business and industry partners on real IT projects, and have successfully supervised a range of Level 7 data science apprenticeship projects. Several academics hold a teaching qualification (Fellow or Senior Fellow or Advanced HE). Nearly all have a PhD in Computing or Mathematics.

In addition to the academic team of the School of Computing at Buckingham, high-profile guest lecturers and speakers from academia and industry are invited to deliver seminars and workshops on a range of topics covered in the programme.

The programme is supported by a highly professional and dedicated team of faculty managers, administrators, careers specialists and a team of wellbeing specialists.

Teaching Methods and Resources

Teaching is carried out through a combination of lectures supported by seminars, practical workshops and/or tutorial-style discussions. It is enhanced by virtual learning environments, learning tools and software packages. It is also the philosophy of the University’s faculty to be available to apprentices outside the scheduled session times and to encourage good working relationships between staff and apprentices.
Each apprentice is provided with at least one e-textbook for each module of the programme. Apprentices can borrow physical copies from the library. Where possible, the library will post physical copies to apprentices. Among access to online journals and databases is full access to IEEE Xplore. A host of software packages, including MATLAB is available to download onto a personal computer. The School has two IBM Power 8 servers if apprentices require GPUs to run large-scale experiments. A state-of-the-art room is available for lecturers to conduct live online teaching and to record lecture material.

Enrichment activities

The School of Computing hosts a Computing Seminar series throughout the year for all its staff, students and apprentices. Approximately 24 seminars are held per year (January – December). These seminars are given by industry experts, academics from other HEIs and postgraduate research students in computing. Seminars cover a wide range of topics related to the latest developments in computing. All apprentices are invited to participate in computing seminars.

Assessment methods

A range of assessment techniques are utilised throughout the degree programme to allow apprentices to demonstrate achievement of the competencies. Individual and group work, presentations, portfolios, quizzes, projects, reflective statements, posters, professional discussions, and viva are examples of the assessment methods of the programme. The assessment of individual modules within each course varies according to the subject. Please check the module information for more details.

As an integrated degree apprenticeship, apprentices who successfully complete the overall programme receive two awards:

• MSc Applied Data Science (Degree Apprenticeship) from The University of Buckingham.
• Degree Apprenticeship Certificate from ESFA.

The MSc programme is assessed and graded according to the details in the programme and module specifications following the University’s Regulations. The Apprenticeship is assessed and graded as per the L7 DTSS End-Point Assessment Plan.

Apprentices must pass both the MSc programme and the apprenticeship (EPA) to receive the two awards. One award cannot be achieved without passing the other.

Quarterly Progress Reviews

Quarterly Progress Reviews are conducted to monitor and review apprentices’ learning journey, evidence portfolio and development programme.

Employment and career progression

Many of our apprentices have received promotions within their current employer whilst others have secured new employment in data science and related roles.

What our apprentices say:

“My apprenticeship helped me to build up the confidence in my skills that allow me to be a part of the success of growing company.”

– Renata Franczak, MSc Applied Data Science (Degree Apprenticeship), (current apprentice from Vitalograph)

“The apprenticeship degree was important to me because I had very little experience working with databases or data visualisation and analytics tools. It would be incredibly hard, if not impossible, for me to transition, but once I started the university course, everything has made so much more sense ever since! I have learned Python which is a broadly applicable programming language, various data visualisation tools, datamining and machine learning techniques, and also understanding digital transformation, leadership and management. All those skills enabled me a smooth transition before I even graduated!”

– Joanna Koda, MSc Applied Data Science (Degree Apprenticeship), 2023

What employers and line managers say:

“Apprenticeships play a pivotal role in the employee development strategy of Vitalograph. By integrating apprenticeships into our talent development framework, we foster a culture of continuous learning, skill enhancement, and innovation. Investment in apprenticeships not only enhances Vitalograph’s operational excellence and service delivery but also contributes to employee engagement, retention, and career progression. As a global leader in respiratory diagnostics, apprenticeships create important development opportunities to future-proof our capabilities and reinforce our pioneering culture of innovation.”

– Ambrose Downey (Chief People Officer, Vitalograph)

“The course has given Renata [Franczak] additional skills and greater insights into how we can better use our data to add value to the business and given her greater skills on how to efficiently manage data related projects to completion.”

– Owen M. McGinty (Vice President Data Services, Vitalograph)

Our Apprenticeship Success Stories

We are incredibly proud of our apprentices’ achievements. Read more Computing Success Stories.

Apprenticeships are fully funded by the employer through the Apprenticeship Levy. The maximum cost for the L7 DTSS Integrated Degree Apprenticeship is £21,000; this may be less, dependent on your Recognised Previous Learning (RPL) which will be assessed at interview through a robust initial skills assessment. There is no cost to the employee undertaking an Apprenticeship.

Your business will be paying the Apprenticeship Levy each month if it:

• Has an annual pay bill of more than £3 million
• Is connected to any companies or charities for Employment Allowance purposes and has a combined annual pay bill of more than £3 million.

The Apprenticeship Levy is an amount paid at a rate of 0.5% of an employer’s annual wage bill and will be paid through Pay as you Earn (PAYE).

We encourage prospective applicants or employers to engage with us three months before the programme’s start date to ensure a smooth onboarding process for all stakeholders.

Please send expressions of interest to: apprenticeships.digital@buckingham.ac.uk

Prospective applicants can apply online: