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Postgraduate study
Computing & Cyber Security

Digital and Technology Solutions (Degree Apprenticeship)


Course overview

This degree apprenticeship has two specialism you can choose from – IT project management and data analytics.

IT project management
Take responsibility for the evolution and development of technology-based solutions and digital transformation projects. You ensure that the delivery of new solutions meets the client’s expectations. You establish close and trusted relationships with business stakeholders and solutions teams to deliver the roadmap, governance and supporting processes. You manage the programme roadmap, communicating milestones and progress updates with client stakeholders.

Data analytics
You explore business data requirements, applied data selection, data curation, data quality assurance and data investigation and engineering techniques. You help businesses organise data and you provide advice and guidance to database designers in using the data structures and associated data components efficiently. You explore data processing to produce data sets for study and perform investigations using techniques including machine learning to reveal new business opportunities. You present data and investigation results along with compelling business opportunities reports to senior stakeholders.

Upon successful completion, you are awarded an MSc in Digital and Technology Solutions.

This degree apprenticeship is ideal if you are employed and your employer is willing to support you through the Government’s apprenticeship scheme. You need to agree your options with your employer’s training manager.

Please refer to the Digital and Technology Solutions Specialist (integrated Degree) apprenticeship standard for more information.

Alternatively, if you wish to self-fund your studies or obtain a student loan, please visit our non-apprenticeship courses in these areas:
Applied Data Science, MSc
Data Science, MSc
IT Project Management, MSc


Course details

Course structure

Core modules

Capstone Project

You undertake a major, in-depth, individual study in an aspect of computing, IT, computer science or digital technology. The Capstone project is drawn from commercial, industrial or research-based problem areas. You research and investigate aspects of a specific computing discipline and then produce a major deliverable (software package or tool, design, prototype, website, model, research findings, results of an experiment, datasets.) You carry out a critical evaluation of your major deliverable, including obtaining third party evaluation where appropriate.

Digital Transformation

Digital transformation is a change process that allows businesses and society to achieve goals such as improving accessibility and quality of digital services, increasing income generation, implementing innovative and generating a collaborative culture through the use of technology. The adoption of emerging technologies is important to businesses and can be fundamental to their survival. But digital transformation is not all about technology.

You gain an in-depth understanding of the context of digital transformation, its challenges and possible future as well as its leadership and management. You gain the skills to develop a digital transformation strategy for an organisation, by engaging in critical assessment of cutting-edge research findings to inform your solution.

Research and Development

You gain the knowledge and skills to understand the research process in computing and digital media, and the necessary skills to undertake your masters project. You learn how to use and critically evaluate previous academic research, and to generate good evidence material to justify their professional practice. This involves you learning about different research strategies and data generation methods and how they fit into the development lifecycle and the evaluation of the user experience, the use of the academic research literature, and research ethics.

Assessment involves you preparing a research proposal which can form the basis of your master's project.


IT Project Management modules

Agile Development

You gain experience of application development utilising an agile development approach. You also take responsibility for determining project aims, objectives, roles, tasks, deliverables, schedules and documentation.

By learning agile principles and applying a specific agile approach (eg SCRUM) in a live simulation of the methodology, the module will help you to prepare for contemporary practice in industry, developing both knowledge and skills in the application of agile principles.

Managing Projects with PRINCE2®

This module explores in detail an established and widely recognised governance standard method called PRINCE2®. Students will work on simulated project brief in order to build their understanding and critically review the governance methodology.

The module will focus on the structure, processes, documentation, terminology but also tailoring of the governance method in order to plan for a particular project.

The students will be assessed by two in-course assessment elements (60% and 40%) requiring them to work as part of a team.

PRINCE2® is a registered trademark of AXELOS limited

Project Management Philosophies and Tools

Contemporary project management is an evolving and extensive discipline that has grown substantially to meet the needs of modern project management demands. This module cultivates detailed and critical awareness of this domain and its expanding range of philosophies, tools and frameworks.

The purpose of this module is to introduce students to the expanding discipline of project management and familiarise them with modern and contemporary project management modalities and the importance of effective project management to organisational functioning, enterprise growth and development.

Risk Management in Projects

We provide you with an advanced level of study in risk management in projects. You explore a range of tools and techniques used in risk identification, qualitative and quantitative risk analysis, risk-planning responses, PERT and risk monitoring. You learn about financial risk, project-appraisal methods and the application of a decision tree within a project.

You gain awareness of probability theory that represents the corner stone of risk management. Invited speakers from the industry give an overview of risk management in different projects; for example IT, finance, construction, and oil and gas. You work in groups on project case studies which apply risk management theory. In addition, you gain hands-on experience in applying the @RISK© tool to a variety of scenarios of risk factors. You develop a deep understanding of the systematic process of risk management and application of @RISK© software.


Data Analytics modules

Big Data and Business Intelligence

You develop your ability to design and implement database, big data and analytics applications to meet business needs. A case study is used to follow the system development lifecycle. You develop a plausible application from inception to implementation for a real-world scenario.

You investigate the issues and technologies associated with implementing and supporting large scale databases and the services that are needed to maintain and access a repository of data. Investigations are undertaken in a number of areas including big data, data warehouses, integrating legacy data, data management and approaches that support the modelling and visualisation of data for a range of use views.

Data Analytics

This module provides you with the core principles and practical skills to apply state-of-the-art computational methods to perform data analytics. The skills are very important in the new horizon of data analysis where existing massive amount of data contains valuable knowledge, which is critical for prediction and decision-making. Due to its characters (3V: volume, velocity, and variety), computational methods are required to extract such knowledge.

You form a solid foundation of both descriptive and predictive analytics, which refers to tools and techniques for building statistical or machine learning models to make predictions and decisions based on data. Practical guidance about how to handle unlabelled, noisy, incomplete, large-scale data is discussed and you learn how to select the best technique to handle different type of data in different scenarios.

Data Visualisation

The field of information visualisation has expanded rapidly with many designers generating new forms of charts through which to view quantitative data. This module explores the range of charts available from the traditional such as bar charts and pie charts, to the more novel such as stream graphs, tree maps, sunbursts, and force diagrams, and examines their mathematical properties.

By accurately representing quantitative data using appropriate charts, the intended audience can make their own interpretations of the data and identify emerging patterns and themes that are more readily recognisable in chart form than in the form of raw data.

Machine Learning

Machine learning is a subfield of computer science concerned with computational techniques rather than performing explicit programmed instructions. You build a model from a task based on observations in order to make predictions about unseen data. Such techniques are useful when the desired output is known but an algorithm is unknown, or when a system needs to adapt to unforeseen circumstances.

You explore statistics and probability theory as the fundamental task is to make inferences from data samples. The contribution from other areas of computer science is also essential for efficient task representation, learning algorithms, and inferences procedures. You gain exposure to a breadth of tasks and techniques in machine learning.

Assessment is an in course assessment (100%).


Modules offered may vary.


How you learn

Lectures are used to introduce and develop material and include research issues and recent developments as appropriate. Explore subjects in-depth through tutor-led seminars, practical workshops, individual or group research, and by contributing to discussion forums.

Develop your intellectual skills through lectures, discussion seminars and online discussions. Modules use directed self-study and research to develop your critical evaluation skills.

Lectures, including presentations from guest external practitioners, deliver relevant subject-specific content. Practical work includes case studies from real scenarios.

Core themes throughout the course include developing your transferable skills, self-managed learning and professional development. Methods include group-based activities and discussions, self-directed learning and research, and tutor-led workshops.

How you are assessed

You are assessed by individual coursework, including case studies and essays, with critical evaluation of processes or products, and evidence of research into a specified area. We are very experienced in assessing group work with a research profile in that area. Your assessment starts within a group, with a clear emphasis on process as well as product, but proceeds to your individual assessment by a variety of means.

You are also assessed through an individual portfolio of technical work to a professional standard and a presentation to tutors on your research findings.


Entry requirements

Typically a 2.2 or higher in a relevant subject, including but not limited to: artificial intelligence, computing, computer science, data science, information technology.

A good GCSE profile is expected, including English language and maths grade 4 (grade C) or above (or eligible equivalent). Apprentices without level 2 English and maths will need to achieve this level prior to taking their apprenticeship End Point Assessment.

For general information please see our overview of entry requirements



Career opportunities

Job roles from the IT project management route include IT delivery manager, web delivery manager, IT development manager, and software product management specialist.

Job roles from the data analytics route include big data analyst, data and insight analyst, data science specialist, data management specialist and analytics lead.

Degree apprenticeships combine practical work experience with studying the technical knowledge through a higher education qualification. All apprentices are already be in employment.

Benefits for employers and apprentices:

  • increasing future productivity
  • keeping the business up-to-date with the latest knowledge and innovative practice
  • delivering on-the-job training to employees tailored to business needs
  • tackling skills shortages by filling higher level skill gaps
  • developing and retaining existing staff by offering support and a fresh perspective
  • improving employees’ career prospects.



  • Not available full-time


2022/23 entry

Fee for UK applicants

for the full course

More details about our fees

  • Length: 2 -3 years
  • Attendance: Day release (normally one day a week)
  • Start date: September or January

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