Gain the expertise to thrive in the rapidly evolving digital industry
University of Gloucestershire’s 100% online MSc Computer Science provides a comprehensive foundation in computing, equipping you with the skills to solve complex technological challenges and drive business success. You’ll explore key areas such as software development, database management, networking, and system architecture while applying theoretical knowledge to real-world scenarios.
Tailor your studies to align with your career goals by choosing from four specialised pathways: cyber security, data analytics, artificial intelligence, and software engineering.
Designed to accommodate your professional and personal commitments, our MSc Computer Science prepares you to tackle advanced computing challenges and step into leadership roles across a wide range of sectors—from global tech firms to innovative start-ups and digital enterprises.
Course overview
This course develops your expertise in key areas of modern computing, with a strong focus on turning data into actionable insights and securing digital infrastructure.
You’ll study how to extract meaningful patterns from large datasets, apply predictive models, and use business intelligence tools to inform strategy. You’ll also learn to manage information security risks and design robust systems that account for both technological and human factors. By blending practical skills with critical thinking, this degree equips you to support digital transformation in business, government, and beyond.
Skills and knowledge
Graduates of this course will be able to:
Solve complex computing problems by identifying business-focused technical solutions
Assess software architecture to understand trade-offs and scalability impacts
Build leadership skills that align solutions with user needs
Deliver innovative solutions for simple and complex system implementations
Design real-world technical solutions that enhance business performance
Entry requirements
To study Gloucestershire’s 100% online MSc Computer Science, you will need to have completed:
A 2:2 undergraduate honours degree, or a comparable professional qualification. (Applicants with significant work experience but without a formal Level 6 qualification will also be considered.)
OR
A minimum of three years of relevant work experience (CV required, including one reference).
English language requirements
If English is not your first language, or if your most recent education or work experience was not conducted in English, you must demonstrate proficiency through an approved English language test. This includes IELTS (minimum overall score of 6.0, with no individual component below 5.5) or an equivalent qualification.
Modules
Principles of Programming Languages
Explore the evolution of programming languages and the core abstractions they use, including data types and control structures. This module introduces key paradigms through practical examples from languages like Java and Lisp, while also covering advanced features such as exceptions and polymorphism. You will gain a foundational understanding of how programming languages are structured and the rationale behind their design.
Data Warehousing and Data Mining
This module introduces concepts, theories, and fundamentals of database management systems (DBMSs) and bridges you to practical and hands-on experience with databases and data mining. The module covers essential topics such as database design, relational models, normalisation, structured query language (SQL), and data mining techniques. The module also covers advanced topics such as query processing and optimisation and data mining of data streams and text data.
Information Security Management
Examine the strategic, technical, and human factors involved in securing information systems. You’ll study security planning, risk management, policies, governance models, and emerging threats. This module prepares you to implement robust security strategies across IT environments while also considering ethical, regulatory, and management perspectives.
Business Intelligence & Visualisation Tools
Discover how to turn organisational data into valuable insights using business intelligence tools and visualisation techniques. You’ll explore BI concepts such as data organisation, reporting, OLAP, and dashboard creation. This module focuses on using data to support strategic decision-making and presenting findings through clear, visual formats.
Algorithms & Data Structures
Learn to design and evaluate the algorithms and data structures that form the backbone of effective software development. This module focuses on analysing efficiency, understanding core data objects such as arrays, stacks, queues, trees, and graphs, and implementing algorithmic patterns. You'll also explore techniques for enhancing code performance, reusability, and optimisation to support scalable software solutions.
Computer Architecture
Examine how computer systems are designed and how their architecture supports business and technical needs. You’ll explore hardware and software components, system functionality, and decision-making strategies for implementing performance-optimised, usable solutions. By the end of the module, you'll be able to assess architectural designs aligned with organisational requirements.
Operating Systems
Gain a comprehensive understanding of how operating systems function, including process and memory management, file systems, device drivers, and concurrency. This module explores both design principles and practical implementations across systems like Windows, iOS, Linux, and distributed networks. Case studies will help you evaluate OS choices and their implications for system design and software development.
Computer Network Security
Develop a solid foundation in the principles and practices of network security. You’ll analyse vulnerabilities in LANs, WANs, databases, and operating systems while exploring risk assessment, intrusion detection, encryption, authentication, and access control. This module equips you to design secure systems and apply the right security measures for diverse communication environments.
Machine Learning
This module is an introduction to machine learning with R. The students will learn how to process and analyse various data sets. Topics covered include exploratory data analysis (EDA), unsupervised learning (e.g., PCA, k-means), and supervised learning (e.g., linear regression, logistic regression). Machine learning techniques such as test-train-split, k-fold cross-validation, and regularisation will also be covered. By the end of the module, students will be able to write code to apply machine learning techniques and methodologies mentioned above to real data sets.
Research Methods for Computer Science
Develop your ability to conduct academic research in computer science. You’ll explore research design, methodology, and epistemological frameworks, while learning how to critically review literature, develop a proposal, and ensure ethical compliance. The emphasis is on creating a robust foundation for a future research project or dissertation.
Applied Research for Computer Science (30 credits)
Apply your research knowledge to a real-world computer science problem by developing a thesis or project. Building on your research methods training, you'll define a problem, design and execute a research strategy, collect and analyse data, and present solutions or insights, demonstrating your ability to conduct independent, applied research.
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