Brayford Pool
3-4 years
6 years
The BSc Computer Science with Cloud Computing programme offers an exciting curriculum designed to equip you with the foundational knowledge and advanced practical skills necessary to excel in the fast-growing cloud computing industry.
The programme integrates core computer science principles with a strong emphasis on the design, deployment, and maintenance of modern cloud-based solutions. The curriculum is also aligned with industry-recognised cloud vendor certifications, enabling you to build and validate industry-transferable competencies in critical areas of cloud computing.
By gaining expertise in industry-standard practices and technologies, you can prepare for roles such as a cloud developer, solutions architect, or data analyst.
Build your skills through practical application of knowledge
Learn from industry-experienced experts
Gain industry experience through placement opportunities
Attain industry-recognised certifications as part of the programme
Use dedicated computer laboratories for your work
Industry-informed curriculum
You can build extensive knowledge of cloud computing concepts, encompassing cloud-based computers, storage, and networking, whilst developing practical skills in cloud architecture design, deployment, and development.
A key focus is on cloud service deployments, a theme woven throughout the curriculum and delivered through topics such as Infrastructure-as-Code (IaC) and serverless computing for API development. Cloud data services are also a key topical area, with attention given to data transformation and analysis using cloud data platform.
You can learn through a blend of lectures and workshop sessions where you can gain hands-on experience. In your final year you may also can explore a specific area of cloud computing and undertake in a significant cloud-based project.
You will also be expected to undertake two to three hours of self-directed study for every hour taught.
The module aims to introduce the concepts of Algorithms and Complexity, providing an understanding of the range of applications where algorithmic solutions are required.
Students will have the opportunity to be introduced to the analysis of time and space efficiency of algorithms; to the key issues in algorithm design; to the range of techniques used in the design of various types of algorithms. Students can also be introduced to relevant theoretical concepts around algorithms and complexity in the lectures, together with a practical experience of implementing a range of algorithms in the workshops.
This module aims to introduce the fundamentals of computer hardware underpinning the key aspects of Computer Science. This knowledge is not only essential for deeper understanding of the governing processes behind computing but also for realising how hardware interacts with software.
By studying Computer Architecture, students can gain greater confidence in their study subject and future benefits when improving their programming skills. The module will study the individual components of a computer system, their function, main characteristics, performance and their mutual interaction.
Data science is a relatively new field of study that utilises algorithms, statistics, and visualisation methods to answer scientific questions using data. In this module, students learn how to load, transform, visualise, and extract knowledge from data using their skills as programmers. Students can also gain experience in using interactive programming environments (e.g. IPython/Jupyter) and open-source libraries (e.g., numpy, matplotlib, pandas) that are widely used by data scientists in industry. In the latter part of the module, students will work in groups to analyse a real-world dataset and present their findings to their peers.
This module aims to equip students with mathematical knowledge and skills required to design and develop computer systems and software. Representative topics include sets, relations and functions, logic, algebra, basic statistics, and probability theory. The critical role of mathematics in Computer Science and Games Computing and will be demonstrated with applied examples.
This module extends the concepts and practice of simple computer programming, with attention paid to the essentials that constitute an object-oriented computer program including layout, structure, and functionality. The module aims to extend students' knowledge of computer programming and introduces them to the object-oriented paradigm and related concepts applied to algorithm and software development. There is also emphasis upon the use of version control and its role in archiving and facilitating software development.
In this module students will have the opportunity to study both the theoretical design concepts which underpin all operating systems and, through case studies, the practical implementation techniques of current operating systems. Special attention will be given to shell programming languages and examples, to practically implement concepts and techniques at the basis of the various operating systems.
This module introduces students to software constructs and the development of simple programs using a high-level programming language. Simple design concepts and standard programming practices are presented, and attention is paid to the fundamentals that constitute a complete computer program including layout, structure, and functionality. Additionally, the fundamental computing data structures allowing the representation of data in computer programs are explored and implemented.
This module aims to provide a comprehensive analysis of the general principles and practices of advanced programming with respect to software development. Notions and techniques of advanced programming are emphasised in the context of analysis, design, and implementation of software and algorithms. Great importance is placed upon the Object-Oriented paradigm and related concepts applied to algorithm and software development using the C++ programming language, however students will also be exposed to the principles and underlying theories pertaining to functional programming.
The module aims to provide a modern introduction to the concepts of symbolic artificial intelligence, set in the context of intelligent agents.
The module covers the concepts such as state space representations and search, heuristic and adversarial search methods, and optimization techniques. The module also covers knowledge representation, AI planning, and some nonstatistical, machine learning methods.
This module explores Cloud Computing and Data Centre technologies for the virtualization of computing infrastructure. The module will cover the core theory and concepts that support the virtualisation of compute, storage, and networking resources. Students will also learn about the design and development of web services and associated data formats, with scalability and data interoperability key considerations. The ethical, legal and security implications of adopting cloud computing services are also discussed.
In this module, students learn how computers can be used to analyse and process the natural language that we use in our everyday lives. Natural language is a data type like no other, and presents a unique set of challenges for which the field of Natural Language Processing (NLP) has sought to provide answers. Common applications of NLP include machine translation, text summarisation, question answering, chatbots, grammar checking, and many others.
This module considers basic computer communications and networking with an emphasis on the Internet Protocol.
The module examines the Internet Protocol as a model for intercommunication in modern network implementations. Additionally the module examines fundamental design features of a Network Protocol and the need to implement security in the modern Internet.
The module adopts a standards driven approach and determines methods used in modern network systems for the distribution of data. An emphasis on network infrastructure and protocols underpins the module together with basic security considerations important in modern network architectures. Aspects of security concepts are extended to consider mechanisms that counter various forms of threat that exist from different sources.
This module explores the fundamental concepts of designing, implementing, and using database technologies and students are expected to develop a conceptual view of database theory and then transform it into a practical design of a database application. Alternate design principles for implementing databases for different uses, for example in social media or gaming contexts are also considered.
This purpose of this module is to provide students with the experience of working as part of a team within a simulated commercial setting. Students will go through the key phases of software development from ideation through to development, testing, delivery, and publishing. Through the module students will learn how to manage and deliver commercial software development projects. This will include ethical, social and professional issues, project management, communication, time management, and team working strategies.
This module develops on the skills learnt in the first year and places them in a simulated commercial setting. The artefact produced as part of the software development process should be suitable for inclusion within a professional portfolio.
The module aims to introduce the main concepts of Autonomous Mobile Robotics, providing an understanding of the range of processing components required to build physically embodied robotic systems, from basic control architectures to spatial navigation in real-world environments.
Students will have the opportunity to be introduced to relevant theoretical concepts around robotic sensing and control in the lectures, together with a practical “hands on” approach to robot programming in the workshops.
The module introduces the fundamentals of data science and big data analytics, an emergent specialised area of computer science that is concerned with knowledge on ‘Big Data’ mining and visualisation, including state-of-the-art database platforms, development toolkits, and industrial and societal application scenarios. Students can be exposed to core Big Data analytics concepts and models, the current technology landscape, and topical application scenarios using a variety of simulation environments and open datasets.
Inspired by the biological neurons that make up our brains, artificial neural networks (ANNs) are simple mathematical models that date back to the work of McCulloch and Pitts in the 1940s. Today, ANNs are the powerhouses of modern AI solutions and are regarded as one of the most important technical innovations of the past decade. In this module, we will follow the chronology of the deep learning revolution, starting with the basics of deep feed-forward networks and how to train them effectively. We will then work our way forwards in time and study convolutional neural network (CNN) architectures for images, recurrent neural networks (RNN) for time series data, and unsupervised models for representation learning. Towards the end of the module, students explore how deep nets learn and study the issues that can impact their real-world utility and the implications for society at large.
Digital image processing techniques are used in a wide variety of application areas such as computer vision, robotics, remote sensing, industrial inspection, medical imaging, etc. It is the study of any algorithms that take image as an input and returns useful information as output.
This module aims to provide a broad introduction to the field of image processing, culminating in a practical understanding of how to apply and combine techniques to various image-related applications. Students will have the opportunity to extract useful data from the raw image and interpret the image data — the techniques will be implemented using the mathematical programming language Matlab or OpenCV.
The module introduces the fundamentals of machine learning and principled application of machine learning techniques to extract information and insights from data. The module covers supervised and unsupervised learning methods. The primary aim is to provide students with knowledge and applied skills in machine learning tools and techniques which can be used to solve real-world data science problems.
This module offers students the chance to demonstrate their ability to work independently on a significant, in-depth project requiring the coherent and critical application of computer science theory and skills.
Students must initially produce a project proposal and related materials to frame the work, specifying clear, specific, academically justified, and appropriately scoped aims and objectives, as well as feasible means for fulfilling those aims and objectives. Students then work independently to fulfil those project goals. Throughout this process students are expected to demonstrate the application of practical development and analytical skills, innovation and/or creativity, and the synthesis of information, ideas and practices to generate a coherent problem solution.
† Some courses may offer optional modules. The availability of optional modules may vary from year to year and will be subject to minimum student numbers being achieved. This means that the availability of specific optional modules cannot be guaranteed. Optional module selection may also be affected by staff availability.
We want you to have all the information you need to make an informed decision on where and what you want to study. In addition to the information provided on this course page, our What You Need to Know page offers explanations on key topics including programme validation/revalidation, additional costs, and contact hours.
On the course you can access industry-standard facilities and dedicated computing labs to support your studies and enable you to hone you skills. This includes access to high-performance PCs with Nvidia GPUs as well as access to cloud vendor platforms, for example Microsoft Azure and Google Cloud Platform, for real world cloud service deployments.
Students on the course have the opportunity to gain cutting-edge industry certification in areas such as Cloud Data and Cloud AI services. Examples of the certification exams and digital badges you can achieve as part of the programme include Microsoft Azure Data Fundamentals and Microsoft Azure AI Fundamentals.
This programme gives you the option to complete a sandwich year. If you choose the sandwich placement option, you take a year out to work in industry and gain experience between the second and third year. If you wish, this can be abroad. You will be supported throughout your placement. A Placement Year Fee is payable to the University of Lincoln during this year for those joining in 2025/26 and beyond. You are expected to cover your own travel, accommodation, and living costs. There are also opportunities to take shorter work placements, for example over the summer period, and to be involved in systems development projects for real industry clients.
A variety of assessment methods are used on the programme. These can include in-class tests, individual and group coursework, and poster sessions. A significant amount of the overall assessment is weighted towards coursework, emphasizing hands-on experience and problem solving.
104 UCAS Tariff points from a minimum of 2 A Levels or equivalent qualifications.
BTEC Extended Diploma: Distinction, Merit, Merit.
T Level: Merit
Access to Higher Education Diploma: 45 Level 3 credits with a minimum of 104 UCAS Tariff points.
International Baccalaureate: 28 points overall.
GCSE's: Minimum of three at grade 4 or above, which must include English. Equivalent Level 2 qualifications may be considered.
The University accepts a wide range of qualifications as the basis for entry and do accept a combination of qualifications which may include A Levels, BTECs, EPQ etc.
We may also consider applicants with extensive and relevant work experience and will give special individual consideration to those who do not meet the standard entry qualifications.
Non UK Qualifications:
If you have studied outside of the UK, and are unsure whether your qualification meets the above requirements, please visit our country pages for information on equivalent qualifications.
https://www.lincoln.ac.uk/studywithus/internationalstudents/entryrequirementsandyourcountry/
International students will be required to demonstrate English language proficiency equivalent to IELTS 6.0 overall, with a minimum of 5.5 in each element. For information regarding other English language qualifications we accept, please visit the English Requirements page.
If you do not meet the above IELTS requirements, you may be able to take part in one of our Pre-sessional English and Academic Study Skills courses.
The University of Lincoln's International College also offers university preparation courses for international students who do not meet the direct entry requirements. Upon successful completion, students can progress to Bachelor's study at the University of Lincoln. Please visit https://www.lincoln.ac.uk/internationalcollege/ for more information.
For applicants who do not meet our standard entry requirements, our Science Foundation Year can provide an alternative route of entry onto our full degree programmes:
https://www.lincoln.ac.uk/course/sfysfyub/
If you would like further information about entry requirements, or would like to discuss whether the qualifications you are currently studying are acceptable, please contact the Admissions team on 01522 886097, or email admissions@lincoln.ac.uk.
The best way to find out what it is really like to live and learn at Lincoln is to visit us in person. We offer a range of opportunities across the year to help you to get a real feel for what it might be like to study here.
