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International Bachelor’s Degree (Honours) in Computer Science

BACHELOR OF SCIENCE (HONOURS) IN COMPUTER SCIENCE (TOP-UP) (E-LEARNING)

12 months Full Time / 24 months Part Time Instructor-led Live & Mentor-led Blended Learning



Earn an International Bachelor’s Degree Honours in Computer Science, Awarded by University of Roehampton, UK

What do I Get?

Acquire Data Engineering Skills   

Learn to blend the tools and methods of data management and processing with software engineering principles. ​

Acquire Data Visualization Skills

Learn statistical techniques and visualization presentation skills for results obtained from data analysis​.

Develop Fluency in Computer Security​

Learn to incorporate the ideas from ethical practice, risk management, legal considerations, and technology-based solutions to address computer security issues.​

Explore Machine Learning Techniques​

Explore how machines can learn from an existing data to provide stochastic systems that perform tasks based on patterns and inference. 

Execute a Mentor-led Project in Computer Science​

Apply practical and analytical skills to produce project deliverables that meet a real need in a wider context​. 

Audience and Certificates

Target Audience

  • Candidates who have completed a Polytechnic Diploma
  • Candidates who have completed a Higher National/Higher Diploma
  • Candidates who have completed Lithan Academy’s Higher Diploma (Diploma & Advanced Diploma) in Software Engineering with ‘Pass’ grade
  • Candidates who want to earn an international bachelor’s degree
  • Candidates with relevant work experience

Prerequisite

Age: Minimum 18 years ​

Academic Qualification: 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. (CV of the candidate is must)​

Experience: Not mandatory for other than mature candidates

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

  • Minimum attendance of 75% in all the sessions.
  • Should achieve minimum pass grade in the assessment of each module.

Certificate(s)

  • Bachelor of Science (Honours) in Computer Science by University of Roehampton

Blended Learning Journey

(1200 Hours)

Flipped Class/Mentoring

96 hours

Projects / Assignments

122 hours

Self Learning

982 hours

Modules

Data Visualisation

Data Visualization explores the art and science of visual descriptive statistics. This module starts by introducing the principles of data visualization and the process of visualization design. Visualization design features throughout the module, as the students are introduced to the perceptual and cognitive foundations of visualization, and the core visualization techniques for different types of data. The module concludes by examining how visualizations can be evaluated via user studies and using the results the students gather from these studies further in data reporting scenarios.​ 

Data Visualization also incorporates web development as the interactive visualizations developed will be presented via a web platform. Students will develop their visualizations using a suitable web framework and deploy their visualizations appropriately. The web development aspect will require students to apply both front-end and back-end development processes to present the data.​ 

Data Visualization 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. This 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 visualization.  The aim is to ensure that you understand how to present your results with accuracy and in an engaging format. 

Session Plan

More Details

Learning Outcome

Knowledge

By the end of this module, you will gain following knowledge:

  • Gain knowledge by working in groups on design-based tasks using the different techniques introduced in the lectures​
  • Gain hands-on practice via practical labs to build visualisations using a suitable web framework, applying the principles taught in the lectures and tutorials

 

Skills

By the end of this module, you will acquire following skills:

  • Review an existing web application against a current web standard​
  • Analyse the effectiveness of a given visualization for a particular task​
  • Analyse and select visualization techniques for specific problems
  • Conduct and report on a study that utilizes both qualitative and quantitative evaluation

Machine Learning

Machine Learning explores how machines can learn from existing data to provide stochastic systems that perform tasks based on patterns and inference. This module introduces what machine learning is and examines different approaches to machine learning, including decision trees and neural networks. 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 also focuses on tools, algorithms, and libraries that are applied to data sets to build systems that can perform intelligent tasks. You will work with a variety of tools based on the type of technique being explored during your study. You will work in programming languages best suited for the tool being used.​ 

Machine Learning provides the capstone to the algorithms and the artificial intelligence theme within Computer Science. The aim is for students to have fluency in the latest tools used in a variety of industries to perform automation. 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

Session Plan

More Details

Learning Outcome

Knowledge

By the end of this module, you will gain following knowledge. 

  • Gain knowledge by exploring the machine learning techniques presented in a collaborative work environment. 

Skills

By the end of this module, you will acquire following skills:

  • Apply simple statistical learning algorithms such as Naive Bayesian Classifier to a classification task and measure the classifier's accuracy 
  • Evaluate the performance of a simple learning system on a real-world dataset 
  • Compare and contrast each of the following techniques, providing use case examples where each strategy is superior: decision trees, neural networks, belief networks 
  • Evaluate the ethical concerns of applying machine learning techniques to a real-world dataset 

Data Engineering

Data Engineering examines how software engineering practices are applied to the development of modern data pipeline solutions that drive data-driven decisions and business strategies. The module begins by exploring parallelism concepts which allow students to understand the benefits of building distributed data platforms. Data Engineering also delves into the 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 on to examine 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 solutions more sophisticated than 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 comfortably apply the team-working techniques provided in software engineering. 

Session Plan

More Details

Learning Outcome

Knowledge

By the end of this module, you will gain following knowledge:

  • Gain hands-on practice via practical labs by using the tools for delivering data pipeline solutions. This is an opportunity for students to work as a team in a lab environment. 

Skills

By the end of this module, you will acquire following skills:

  • Evaluate concurrency and parallelism approaches to support data engineering solutions​
  • Evaluate simple strategies for executing a distributed query to select the strategy that minimizes the amount of data transfer.​
  • Identify appropriate transaction boundaries in application programs​
  • Evaluate the time to retrieve information, when indices are used compared to when they are not used.​
  • Analyse the requirements for building a data delivery pipeline.​
  • Work as a member of a development team recognising the different roles within a team and different ways of organising teams

 

Cyber Security

Cybersecurity explores the risks and their mitigations inherent to computer and internet use. This module incorporates ideas from ethical practice, risk management, legal considerations, and technology-based solutions to address computer security issues. Cybersecurity begins by examining the concept of privacy from a philosophical, legal, and ethical standpoint, before exploring some of the technologies 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, Cybersecurity explores secure design and the use of cryptography in computer systems. Finally, human factors, including interface design and governance are explored.​ 

Cybersecurity 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. Cybersecurity 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 the Cybersecurity module is to develop the students’ fluency in computer security. The module capstones the Systems and Cybersecurity theme of Computer Science, insofar that an understanding of the system is required to fully appreciate issues of computer security. The module will require you to undertake evaluation of systems to understand vulnerabilities and mitigations.  

Session Plan

More Details

Learning Outcome

Knowledge

By the end of this module, you will gain following knowledge:

  • Learn how to apply the principles of security analysis and secure design using problem-based learning in groups​
  • Gain hands-on practice via practical labs by using the tools of security practitioners and build secure applications

Skills

By the end of this module, you will acquire following skills:

  • Evaluate solutions to privacy threats in transactional databases and data warehouses.​
  • Investigate measures that can be taken by both individuals and organizations including governments to prevent or mitigate the undesirable effects of computer crimes and identity theft.​
  • Analyse the trade-offs of balancing key security properties (Confidentiality, Integrity, and Availability).​
  • Evaluate risks to privacy and anonymity in commonly used applications.​
  • Evaluate the purpose of cryptography and the ways it is used in data communications.​
  • Describe the issues of trust in interface design with an example of a high and low trust system.

Final Year Project

The final year project allows you to explore a topic of your choosing based on your interests as agreed and supported by 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 your program. 

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. Your goal is to produce a product and supporting report. A final-year project showcase with external partners will finalize the module​ 

Students are supported in their endeavor via a series of workshops focusing on different elements of the project lifecycle, including:​  

  • Problem elucidation and objective setting. 
  • Performing an investigation into the literature and context of a project. 
  • Conducting an evaluation. 
  • Delivering a significant piece of written work 

Session Plan

More Details

Learning Outcome

Knowledge

Students are supported in their endeavour via a series of workshops focusing on different elements of the project lifecycle, including:​

  • Understand Problem elucidation and objective setting.
  • Learn how to perform an investigation into the literature and context of a project.​
  • Learn how to conduct an evaluation.​
  • Learn how to deliver a significant piece of written work

Skills

By the end of this module, you will acquire following skills:

  • Self-manage a significant piece of individual work using appropriate project management techniques. ​
  • Synthesise information, ideas, and practices to define a quality solution to a problem​
  • Apply practical and analytical skills present in computer science as a whole​
  • Produce a project deliverable that meets a real need in a wider context.​
  • Critical self-evaluation of the overall project process and deliverables.​
  • Recognise the legal, social, ethical, and professional issues relevant to a project.​
  • Produce a report that describes and summarises the entire project deliverable and process, including evaluation

Pricing and Funding

USD 4800.00

Pricing

Total Course Fee

Detailed Breakdown

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