MSc Advanced Computer Science

Turn your computing understanding into computing expertise.

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As computing continues to evolve at a remarkable pace, the expertise to harness the latest technology becomes more vital every day for organisations in every sector. Our Advanced Computer Science masters provides expertise – helping you develop the specialist skills for a career in computing.

You’ll cover core units exploring themes around high-performance computing, big data, data science, advanced networks and operating systems. From there, you’ll have the flexibility to set the direction for your studies – first, with two option units covering areas like enterprise programming, cryptography or mobile computing, then with a masters project of your own design. While you’ll have the one-to-one support of your supervisor, it’s a solo project – as much about developing computing skills as it is about building confidence and autonomy, vital skills for the workplace.

By the time you’re finished, you’ll be equipped with both theoretical understanding and practical abilities in computer science ­– ready to apply your advanced knowledge to real-world situations. In short, you’ll be ready to begin a rewarding career path in the field.

Non means-tested loans of up to a maximum of £10,000 will be available to postgraduate masters students - click here to find out more information

Features and Benefits

Accreditations, Awards and Endorsements

Career Prospects

The advanced skills and understanding you’ll develop on our MSc course can open the doors to roles across a range of industries, in both the public and private sector. Study with us and you’ll follow in the footsteps of graduates who have gone on to become software developers, programmers, app developers, web and multimedia developers and software testers (to name a few), working with the likes of Barclays, Autotrader, AZoNetwork and Realtime Despatch.

Your masters could also provide a foundation for further studies – going on to do a PhD – whether staying with Manchester Met or going elsewhere.

You’ll have specialist careers support of our careers service from day one, and for three years after you leave us. Between the services available through the Department of Computing and Mathematics and the University’s Career Service, you’ll have access to everything from employer events and careers fair to employability skills workshops and dedicated advisors. 

Learn more about graduate careers

Entry requirements

You’ll need a good UK honours degree – at least a 2:2 – or the international equivalent, in a computing-related subject. We’ll also consider your application if you have a lower-level computing qualification, together with substantial experience in a computing-focused role.

International students please see

Overseas applicants will require IELTS with an overall score of 6.5 with no less than 5.5 in any category, or an equivalent accepted English qualification. Accepted English qualifications can be viewed here.

Pre-sessional English courses are available to support you meeting the English language requirements. Click here for more information

Course details

You’ll start off studying two core units, covering High-Performance Computing and Big Data, Data Science, Advanced Computer Networks and Operating Systems. After that, you’ll have the chance to tailor your studies to your interests and career ambitions, picking two option units to explore areas like enterprise or machine learning. From there, you’ll spend the third term of the year on an in-depth masters project.

Core units:

  • High-Performance Computing and Big Data
  • Introduction to Data Science
  • Advanced Computer Networks and Operating Systems
  • Masters Project

Option units:

  • Cryptography and Encryption
  • Enterprise Programming
  • Mobile and Ubiquitous Computing
  • Data Management and Machine Learning

Click below for unit information.

Read more about this year of study

Core Units

High Performance Computing and Big Data

The aim of this unit is to develop students' knowledge in the areas of parallel and distributed processing, machine learning approaches for handling big data  and current parallel programming models for high-performance computing and big data processing, such as  MPI and MapReduce.

  • Current and Emerging Trends - Evaluation  of current and emerging trends underpinning parallel and distributed systems for high-performance computing and big data – paradigms and platforms,  cloud computing.
  • Features of Big Data - Feature extraction and dimensionality reduction approaches.
  • Artificial Intelligence - Machine learning, AI approaches and their algorithms for handling big data e.g. images, graphs, text.
  • Models and Applications - Programming models and applications for big data and High-performance computing, including MPI, OpenMP, Hadoop/MapReduce, NoSQL with case studies.
  • Professional Context - Professional, legal, ethical, social and cultural issues in high performance computing of big data.
Advanced Computer Networks and Operating Systems

The unit covers advanced topics in computer networks and operating systems. It focuses on principles, architectures, and protocols used in modern large scale networked systems. Topics include:

  • Wide area networks - Compare the characteristics of WAN technologies, including their switching type, throughput, media, security, and reliability; Describe several WAN transmission and connection methods.
  • Virtual networking and remote access - Explain virtualization and identify characteristics of virtual components; Understand VPNs (virtual private networks) and the protocols they rely on; Identify the features and benefits of cloud computing and NaaS (network as a service).
  • Wireless and mobile networking - Wireless links and network characteristics; WiFi: 802.11 Wireless LANs; Cellular internet access; IoT, Sensor networks.
  • Network Management - The Infrastructure for Network Management; The Internet-Standard Management Framework; Quality of Service; Performance and Planning.
  • Classic Operating Systems - Comparing the features and trade-offs of classic operating systems;
  • Virtual Machines - Exploring the need for virtual machines and the means of their implementation;
  • File Systems - Looking at strengths and weaknesses of different approaches to persistent storage;
  • Distributed and Scalable Systems - Focusing on issues related to cloud computing and grid computing;
  • Concurrency, Scheduling & Sharing - Timing and scheduling, particularly in distributed systems;
  • Fault Tolerance - Looking at managing failure in distributed systems.
Masters Project

This unit will involve practical system creation or experimentation work in an area of computing other than digital media. The curriculum is specific to the project you choose but it will include seminars on skills and techniques required for successful design and implementation of research resources, time management, research presentation (oral, written, posters) and professional, legal and ethical issues in computing. Examination of a case study or project in an area appropriate to your intended dissertation work. Where appropriate, the implementation or experimentation may be work-based.

Introduction to Data Science

Introduces concepts, techniques and algorithms for processing and visualising datasets so as to infer useful, actionable knowledge in a domain. The unit develops skills which allow appropriate selection and application of data visualisation techniques for probing and obtaining insights into the nature and the structure of datasets so as to identify and report useful and interesting relationships. A variety of data mining algorithms will be explored with an emphasis on selecting the most suitable for particular data science goals. Students will be able to interpret and explain the data and the models obtained from a data science process, as well as communicate those effectively to stakeholders and make judgements on the use of the data informed by ethical, legal and society issues and implications.

Likely Optional Units

Cryptography and Encryption

The unit covers theoretical discussion of the key encryption algorithms: Diffie-Hellman, RSA, Digital Signatures, modern symmetric cryptosystems DES and AES. Additionally we will code the algorithms and their variants in a modern programming language and implement cryptosystems over a computer network.

Classical Cryptography; Shannons Theory; Block Ciphers; Hash Functions; RSA Algorithm and variants; Discrete and Public-Key Algorithms; Signatures; Pseudo-random number generators; Identification Schemes and Entity Authentication; Key Distribution; Key Agreement Schemes.

Enterprise Programming

Examines key aspects of Distributed Programming and Software Engineering aspects of Enterprise level applications.

  • Distributed computing development [25%] - eg RMI, JMS, JNDI, Jini, Connection to remote services (eg JDBC)
  • Use of a variety of distributed object programming methods [20%] - eg Web Services, REST, SOAP, Ajax, Spring Framework
  • Object wrapping formats and APIs [10%] - e.g. XML, JSON etc
  • Cloud based software development [20%] - e.g. PAAS, SAAS, IAAS
  • Design Patterns, Refactoring, SE design techniques [25%]
Mobile and Ubiquitous Computing

Provides an understanding of the issues, technologies and concepts in mobile and ubiquitous infrastructures, particularly in wireless networks, context-awareness, sensors and programming mobile devices.

  • Explore and evaluate mobile application development frameworks and tools: [50%]
  • Principles in wireless communications: [5%]
  • Research and practise in context and location awareness: [15%]
  • Interfacing to sensors: [15%]
  • Resource discovery and system configuration: [5%]
  • Design of pervasive computing systems: [5%]
  • Examining the literature related to the subject matter: [5%]
Data Management and Machine Learning

The aim of this unit is to develop the student’s knowledge in the areas of data management including online analytical processing; data architectures such as data warehousing and the process and application of machine learning algorithms to data.

  • Data Management Overview [15%] - Example content includes database modelling/querying (relational/noSQL), graph data modelling, applications.
  • Online Analytical Processing (OLAP) [15%] - Including the representation of multi-dimensional views of data; Technologies and Architectures; Categories of OLAP tools, Business Intelligence Tools.
  • Data Warehousing [10%] - Methodologies, architectures, modelling techniques; Data Warehousing Project Management; The Extraction, Transformational and Loading Process;
  • Machine Learning Overview [10%] - The machine learning process, Applications of machine Learning.
  • Machine Learning Algorithms [50%] - For example, artificial neural networks, naïve bayes, decision trees, clustering, association rules, text mining, fuzzy systems, application, analysis and validation.

Assessment weightings and contact hours

10 credits equates to 100 hours of study, which is a combination of lectures, seminars and practical sessions, and independent study. A masters qualification typically comprises of 180 credits, a PGDip 120 credits, a PGCert 60 credits and an MFA 300 credits. The exact composition of your study time and assessments for the course will vary according to your option choices and style of learning, but it could be:


Additional information about this course

Students are expected to behave in a professional and business like manner when on placement or conducting projects with external partners.

Placement options

This MSc course is a short, intensive programme which doesn’t leave much room for a placement. But there are employers who might offer short-term internship opportunities. With our regular ‘meet the employer’ events, together with the support of our careers team, we’ll be there to help you make the most of any available opportunities. 

Department of Computing and Mathematics

Our Department of Computing and Mathematics is a vibrant community of staff and students, which prides itself on internal and external collaboration.

The department is committed to teaching and research that addresses societal challenges through disciplines like artificial intelligence, big data, computational fluid dynamics, cyber security, dynamical systems, the internet of things, smart cities, robotics and virtual reality.

More about the department

Taught by experts

Your studies are supported by a team of committed and enthusiastic teachers and researchers, experts in their chosen field. We also work with external professionals, many of whom are Manchester Met alumni, to enhance your learning and appreciation of the wider subject.

Meet our expert staff


UK and EU students

UK and EU students: Full-time fee: £8,500 per year. Tuition fees will remain the same for each year of your course providing you complete it in the normal timeframe (no repeat years or breaks in study).

Non-EU and Channel Island students

Non-EU international and Channel Island students: Full-time fee: £16,000 per year. Tuition fees will remain the same for each year of your course providing you complete it in the normal timeframe (no repeat years or breaks in study).

Additional Information

A Masters qualification typically comprises 180 credits, a PGDip 120 credits, a PGCert 60 credits, and an MFA 300 credits. Tuition fees will remain the same for each year of study provided the course is completed in the normal timeframe (no repeat years or breaks in study).

Additional costs

Specialist Costs

All of the books required for the course are available from the library. The University also has PC labs and a laptop loan service. However, many students choose to buy some of the core textbooks for the course and/or a laptop. Students may also need to print their assignments and other documents. Campus printing costs start from 5p per page. Estimated costs are £300 for a laptop up to £100 for books and printing. Total optional cost: £400

Placement Costs


Professional Costs

Students can choose to join the BCS - The Chartered Institute for IT at any point in their study. It is not required but is useful. The annual charge is identified for every year there is also an option to take course membership for £57

Other Costs


Postgraduate Loan Scheme

Loans of up to £10,906 for many Postgraduate Courses

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Alumni Loyalty Discount

Rewarding our graduates

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How to apply

The quickest and most efficient way to apply for this course is to apply online. This way, you can also track your application at each stage of the process.

Apply online now

If you are unable to apply online, you can apply for postgraduate taught courses by completing the postgraduate application form. There are exceptions for some professional courses – the course information on our on-line prospectus will give you more information in these cases.

Please note: to apply for this course, you only need to provide one reference.

You can review our current Terms and Conditions before you make your application. If you are successful with your application, we will send you up to date information alongside your offer letter.


Programme Review
Our programmes undergo an annual review and major review (normally at 6 year intervals) to ensure an up-to-date curriculum supported by the latest online learning technology. For further information on when we may make changes to our programmes, please see the changes section of our Terms and Conditions.

Important Notice
This online prospectus provides an overview of our programmes of study and the University. We regularly update our online prospectus so that our published course information is accurate. Please check back to the online prospectus before making an application to us to access the most up to date information for your chosen course of study.

Confirmation of Regulator
The Manchester Metropolitan University is regulated by the Office for Students (OfS). The OfS is the independent regulator of higher education in England. More information on the role of the OfS and its regulatory framework can be found at

All higher education providers registered with the OfS must have a student protection plan in place. The student protection plan sets out what students can expect to happen should a course, campus, or institution close. Access our current Student Protection Plan.