International bachelor’s degree in computer science
International bachelor’s degree in computer science
Bachelor of Science (Honours) in Computer Science (top up)
(E-learning)
Duration and Delivery Mode:
12 months (48 weeks) Full-time/ 24 months (96 Weeks) Part-time (Synchronous & Asynchronous E-learning)
12 months (48 weeks) Full-time/ 24 months (96 Weeks) Part-time (Synchronous & Asynchronous E-learning)
International bachelor’s degree in computer science
Bachelor of Science (Honours) in Computer Science (top up)
(E-learning)
Duration and Delivery Mode:
12 months (48 weeks) Full-time/ 24 months (96 Weeks) Part-time
(Synchronous & Asynchronous E-learning)
Course Brief
The BSc Computer Science programme mixes both the theory and practice of computing to prepare students for work in a wide range of IT related roles, including software engineering, data science, and cyber-security. The uniqueness of Computer Science is the focus on collaborative and ethical practice, embedding ethical ideas across the curriculum, and having students work in collaboration as the norm. This focus aims to prepare students for working in the modern IT workplace, going beyond the familiar core of skills-based teaching in computing.
Computer Science is designed around collaborative practice, where students work together to deliver solutions. The programme uses blended and project-based learning to facilitate this approach. Students will learn how to recognise system requirements, design a solution, implement that solution, test and then deliver said solution. Furthermore, the degree is designed using professional body requirements and standards, taken from the British Computer Society Accreditation Criteria, and the Association for Computer Machinery’s Computer Science Curriculum.
For students who want the skills, practices, and professional responsibility to work in the modern evolving technology world, Computer Science at the University of Roehampton is an undergraduate programme that aims for work readiness for all its students, regardless of their background. Unlike other universities’ established curricula, Computer Science at the University of Roehampton is agile, collaborative, and community driven to continuously improve what it delivers to students. This supports the University of Roehampton’s strategy to:
Computer Science is designed around collaborative practice, where students work together to deliver solutions. The programme uses blended and project-based learning to facilitate this approach. Students will learn how to recognise system requirements, design a solution, implement that solution, test and then deliver said solution. Furthermore, the degree is designed using professional body requirements and standards, taken from the British Computer Society Accreditation Criteria, and the Association for Computer Machinery’s Computer Science Curriculum.
For students who want the skills, practices, and professional responsibility to work in the modern evolving technology world, Computer Science at the University of Roehampton is an undergraduate programme that aims for work readiness for all its students, regardless of their background. Unlike other universities’ established curricula, Computer Science at the University of Roehampton is agile, collaborative, and community driven to continuously improve what it delivers to students. This supports the University of Roehampton’s strategy to:
- Develop and promote an engaging portfolio of programmes, which adapt to meet changing student and employer demands.
- Create an environment in which staff can make a positive and lasting contribution to the lives of students and the wider world.
- Create a community that gives all students the opportunity to develop their interests and skills beyond their course of study, encouraging them to grow as individuals.
Knowledge, Skill, Ability Summary
At the end of the course, you will be able to acquire the following:
Knowledge
- Explain the principles of machine learning, including supervised and unsupervised techniques.
- Identify key concepts and applications of computer vision and OpenCV in AI.
- Summarize the fundamentals of deep learning, including artificial neural networks and CNNs.
- Analyze the capabilities and use cases of generative AI models like ChatGPT.
- Describe the stages involved in planning and executing AI projects, from data acquisition to model deployment.
Skills
- Apply Python programming skills for machine learning and computer vision tasks.
- Develop and optimize machine learning models with hyperparameter tuning techniques.
- Implement deep learning pipelines and analyze model performance using TensorFlow.
- Build and deploy ChatBots using Microsoft Power Virtual Agent for business productivity.
- Design and present comprehensive documentation and project presentations for AI solutions.
Ability
Apply AI techniques and tools to solve real-world problems, develop and deploy Machine Learning and Deep Learning models, and contribute to cutting-edge AI projects.Course – Learning Outcomes
- A systematic understanding of the knowledge of systems architecture and its interrelationship with computer science
- A systematic understanding of the knowledge of mathematical, statistical, and computational modelling and their
interrelationships with computer science - A systematic understanding of methods, techniques and tools for information modelling, management, and security
and their interrelationships with computer science. - A systematic understanding of the commercial and economic context and its interrelationship with computer science
- A systematic understanding of management techniques and its interrelationship with computer science
- Analyses new, novel and/or abstract criteria and specifications of problems using an appropriate range of
established computer science techniques. - Aware of personal responsibility and professional codes of conduct in relation to the legal, social, ethical,
and professional issues of computer science and incorporates this into their practice. - Identifies the possibility of new risks and safety aspects within existing knowledge of computer system operation.
- Seeks and applies new theoretical techniques and practical processes to own performance and evaluation
in the specification, design, construction, and evaluation of computer-based systems. - Analyses new, novel and/or abstract data using an appropriate range of established computer science techniques
to determine the extent to which a computer-based system meets the criteria defined for its current use and future development. - Works effectively within a team, supports or is proactive in leadership, negotiates in a professional context and manages conflict.
Blended Learning Journey
(1200Hours)
Learning Activities
(Experiential LEarning)
108 Hours
Learning Activities
(Small Group Learning)
8 Hours
Learning Activities
(Tutorial)
96 Hours
Learning Activities
(GLH)
212 Hours
Learning Activities
(Self-paced Learning)
988 Hours
Module Summary
Module Brief
Data Visualisation explores the art and science of visual descriptive statistics. The module starts by introducing the principles of data visualisation and the process of visualisation design. Visualisation design then plays an important role throughout the module, as the students are introduced to the perceptual and cognitive foundations of visualisation, and the core visualisation techniques for different types of data. The module concludes by examining how visualisations can be evaluated via user studies and using the results the students gather from these studies in a further data reporting scenario.
Data Visualisation also incorporates web development as the interactive visualisations developed will be presented via a web platform. Students will develop their visualisations using a suitable web framework and deploy their visualisations appropriately. The web development aspect will require students to apply both front-end and back-end development processes to present the data stored.
Data Visualisation provides the capstone to the core Data theme in Computer Science. It builds on the statistical techniques and data presentation ideas provided in Data Science. The module allows students to present the results processes the techniques of Data Science, considering different delivery scenarios such as business reporting, data journalism, and scientific visualisation. The aim is to ensure students understand how to present their results in both a correct and engaging manner.
Other Information
Data Visualisation explores the art and science of visual descriptive statistics. The module starts by introducing the principles of data visualisation and the process of visualisation design. Visualisation design then plays an important role throughout the module, as the students are introduced to the perceptual and cognitive foundations of visualisation, and the core visualisation techniques for different types of data. The module concludes by examining how visualisations can be evaluated via user studies and using the results the students gather from these studies in a further data reporting scenario.
Data Visualisation also incorporates web development as the interactive visualisations developed will be presented via a web platform. Students will develop their visualisations using a suitable web framework and deploy their visualisations appropriately. The web development aspect will require students to apply both front-end and back-end development processes to present the data stored.
Data Visualisation provides the capstone to the core Data theme in Computer Science. It builds on the statistical techniques and data presentation ideas provided in Data Science. The module allows students to present the results processes the techniques of Data Science, considering different delivery scenarios such as business reporting, data journalism, and scientific visualisation. The aim is to ensure students understand how to present their results in both a correct and engaging manner.
Other Information
- SSG Module Reference No: CMP020X302
- Module Validity Date: NA
Module Session Plan
Module Brief
Machine Learning explores how machines can learn from existing data to provide stochastic systems that perform tasks based on patterns and inference. The module first introduces what machine learning is, and then examines different approaches to machine learning, including decision trees and neural-networks. The main body of the module focuses on building learning systems from existing data sets, as well as evaluating the performance of the systems developed. Finally, the module examines the use of machine learning in data mining, the ethical concerns related to machine learning, and how biased data sets can lead to biased systems.
Machine Learning focuses on tools, algorithms, and libraries that can be applied to data sets to build systems that can perform tasks in an intelligent manner. Students will work with a variety of tools based on the type of technique being explored that week. Students will work in programming languages best suited for the tool being used.
Machine Learning provides the capstone to the Algorithms and Artificial Intelligence theme within Computer Science. The aim is for students to have fluency in the modern tools used in a variety of industries to perform automation tasks. Students will also understand the ethical concerns of using such systems. The module builds on the basic problem-space searching techniques in Artificial Intelligence by exploring learning techniques that enable a more general intelligence approach to be applied to narrow intelligence problems.
Other Information
- SSG Module Reference No: CMP020X303
- Module Validity Date: NA
Module Session Plan
Module Brief
Data Engineering examines how software engineering practices are applied to the development of modern data pipeline solutions that drive data driven decisions and businesses. The module begins by exploring parallelism concepts which allow students to understand the benefits of building distributed data platforms. Data Engineering then moves into concepts of dealing with large sources of data, including distributed databases, data warehousing, and data lakes. With a thorough understanding of how distribution and large-scale data operates, the module moves to examining data streaming and transaction processing. Finally, the module ends by considering data pipeline solutions in the cloud and how these enable the delivery of data to data scientists.
Data Engineering blends the tools and methods of data management and processing with software engineering principles. The module will continue the experience provided in Software Engineering, so students can further experience working in agile development teams. The tools used in the module will enable students to build more sophisticated solutions that those in Software Engineering, focusing on technology that allows data to be managed and processed at scale.
Data Engineering continues the team-working and system development via a technology-stack approach of Software Engineering. Students are expected to feel comfortable applying the team-working techniques provided in Software Engineering. Data Engineering provides a capstone to the Software Engineering theme and in many regards the software development work students undertake in Computer Science. On completion of this module, students will have delivered at least two significant software solutions as members of a team.
Other Information
- SSG Module Reference No: CMP020X304
- Module Validity Date:NA
Module Session Plan
Module Brief
Other Information
Cyber-Security explores the risks and mitigations inherent to computer use. The module incorporates ideas from ethical practice, risk management, legal considerations, and technology-based solutions to address computer security issues. Cyber-Security begins by examining the concept of privacy from a philosophical, legal, and ethical stand-point, before exploring some of the technology used to protect an individual’s privacy. The module then continues by introducing foundational principles of computer security, including policies, legal frameworks, CIA (Confidentiality, Integrity, Availability), threats, and attacks. With these principles in place, the Cyber-Security explores secure design and the use of cryptography in computer systems. Finally, human-factors, including interface design and governance are explored.
Cyber-Security brings together concepts covered in a range of modules throughout Computer Science, including Computing and Society, Software Development 2, Databases, Operating Systems, and Software Engineering. Cyber-Security explores how the issues introduced in other modules fit within current computer security definitions. The module also explores the technology to support computer security throughout.
The aim of Cyber-Security is to develop students’ fluency in computer security. The module capstones the Systems and Cyber-Security theme of Computer Science, insofar that an understanding of the system is required to fully appreciate issues of computer security. The module will require students to undertake evaluation of systems to understand vulnerabilities and mitigations. This will best place students to understand the requirements of security as they enter the workplace.
Other Information
- SSG Module Reference No: CMP020X305
- Module Validity Date: NA
Module Session Plan
Module Brief
The Final-Year project allows students to explore a topic of their choosing based on their own interests as agreed and supported via a member of the academic team. The project provides an opportunity for students to research and deliver a significant piece of individual work that incorporates the practical and analytical skills presented in their programme.
The Final-Year project will enable students to explore a topic of their choice. There are four project-types planned:
• Student-defined.• Academic-defined (research-based).• Industry-defined.
• Social enterprise.
All projects will be signed-off by an academic supervisor. The students’ goal is to produce a product and supporting report. A final-year project showcase with external partners will finalise the module.
Other Information
- SSG Module Reference No:CMP040X301
- Module Validity Date: NA
Module Session Plan
Target Audience & Prerequisite
Target Audience
- Higher Diploma/ Advanced Diploma in IT/ Computer Science/ Software Engineering
- Higher National Diploma in Computing related from Pearson.
- Polytechnic Diploma holders in relevant studies
- Matured candidates with relevant work experience for minimum 8 years
Prerequisite
- Academic:
- Relevant polytechnic diploma in computer science and related subjects (which are deemed by the University to be equivalent to Level-4 / Year-1 AND Level 5 /Year 2 of the University Course) with an entry requirement of 10 years of formal education [OR]
- Higher Diploma (Diploma and Advanced Diploma) in Computer Science and related discipline with minimum pass grades (which are deemed by the University to be equivalent to Level-4 / Year-1 AND Level- 5 / Year 2 of the University Course) with an entry requirement of 12 years of formal education [OR]
- Higher National Diploma with an overall Merit (60%) or above in a relevant subject area [OR]
- Mature candidate of 30 years and above with 8 years of relevant work experience
- English Proficiency:
- IELTS – 6.0 (with no elements lower than 5.5) [OR]
- Letter from College/University clearly stating the Medium of Instruction of the highest qualification to be English [OR]
- Its equivalent.
Graduation Requirements
Each learner must meet the following requirements to secure academic qualifications and eduCLaaS job role certification.
- Minimum 75% attendance in all sessions.
- Minimum pass grade in the summative assessment of each module
Certificates
Academic Qualification
BSc Computer Science (Top-Up) awarded by University of Roehampton, United Kingdom
BSc Computer Science (Top-Up) awarded by University of Roehampton, United Kingdom
Statement of Attainment: NA
Industry Skills Certification
- WSQ Install and Configure Server (SF)
AZ-104: Microsoft Azure Administrator - WSQ Administer Server (SF)
AZ-800: Administering Windows Server Hybrid Core Infrastructure - WSQ Configure Advanced Server (SF)
AZ-801: Configuring Windows Server Hybrid Advanced Services - WSQ Cloud Administration (SF)
AZ-140: Configuring and Operating Microsoft Azure Virtual Desktop - WSQ Security Administration (SF)
SC-300: Microsoft Identity and Access Administrator
EduCLaaS Job Role Certification
Associate Cloud Administrator
Associate Cloud Administrator
Other Information
SSG Course Reference No: NA
Course Validity Date: NA
Course Developer: University of Roehampton, UK
Course Validity Date: NA
Course Developer: University of Roehampton, UK