Unlock the power of data to drive business success
University of Gloucestershire’s 100% online MSc Computer Science with Data Analytics is designed to equip you with the fundamental skills needed to lead in data-driven roles. You’ll gain a solid foundation in data science, data mining, and analytics, with a focus on applying these concepts in business intelligence and decision-making.
As a student, you’ll learn how to design effective database management systems that support meaningful reporting and research, turning raw data into clear, visual insights. Modules cover a range of analytics tools and systems used in modern business environments, including data warehousing and customer relationship management (CRM) platforms, highlighting their strategic importance.
Built to flex around your professional and personal commitments, this MSc equips you to lead data-driven innovation across industries—whether in global organisations, tech start-ups, or forward-thinking ventures.
Course overview
Designed for professionals seeking to enhance their analytical capabilities, this course equips you with practical skills to interpret data and drive business performance. You’ll gain hands-on experience with tools such as Python, R, SQL, and OLAP systems while exploring key areas including data mining, visualisation, and business intelligence.
You'll also develop your ability to manage and analyse large-scale databases, applying advanced modelling techniques to solve real-world organisational challenges.
Skills and knowledge
Graduates of this course will be able to:
Solve complex computing problems to meet business requirements effectively
Apply architecture knowledge to assess trade-offs and sustainability
Discuss dashboard design using models, algorithms, and techniques
Create data visualisation solutions based on project needs
Implement architectural solutions with modelling, processing, and performance goals
Entry requirements
To study Gloucestershire’s 100% online MSc Computer Science with Data Analytics, 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 mining and Analysis
Develop a foundational understanding of data mining and analytics within the context of business intelligence. You will explore key data science principles, uncovering how data patterns, structures, and algorithms can be applied to solve real-world problems. Emphasis is placed on practical skills in programming languages such as Python, R, SQL, or Java to build data models, apply testing strategies, and execute analytical techniques. You will also gain experience in data processing and the design of data warehouses, CRM systems, and OLAP tools, with examples drawn from emerging applications such as eCommerce platforms.
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.
Advanced Database Management Systems
Explore advanced concepts in database design and management, including query optimisation, distributed databases, big data handling, and data modelling. You will learn how to extract and analyse data to support effective business decision-making. Practical experience with SQL and tools such as Oracle will allow you to explore performance tuning and evaluate different database architectures. By the end of the module, you will be equipped to document data models, assess knowledge management strategies, and develop tailored database solutions for various business scenarios.
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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