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This course is closed to international applicants for 2021 entry.

Course overview

Computer Science is playing a key role in many industries all around the world. Developments in artificial intelligence, apps and cybersecurity are changing how we live, work, and socialise. This two-year masters provides a more in-depth study of taught modules with a full-year research project.

Taught modules in your first year will develop your knowledge in key topics such as user experience design, artificial intelligence, and data analysis. Optional modules allow you to to study specialist areas, including machine learning, cyber security and autonomous robotics.

In your second-year research project, you will get the opportunity to work with an industry partner to build your experience and connections. You can also choose to work with one of our 'world-leading' research groups. Previous projects have included:

  • Deep Learning for Plant Phenotyping
  • Automated algorithm design
  • A mobile application to prevent and cure obesity

If you choose to focus your study and research in the field of AI, you can graduate with a degree titled ‘MSc Computer Science (Artificial Intelligence)’. No computer programming experience is needed.

Why choose this course?

Scholarship available

There are three levels to the award which range from 10-50% off your tuition fees.

Top 10

University in the UK, ranked by research power

Research Excellence Framework 2014

Ranked 6th in the UK

For universities targeted by the largest number of top employers in 2019-2020

High Fliers Report The Graduate Market 2019-2020

96.4% of postgraduates

from the School of Computer Science secured work or further study within six months of graduation

HESA Graduate Outcomes 2020, using methodology set by The Guardian

Multiple pathways

No computer programming experience is needed.

Your modules will depend on your background in computer science and maths

Course content

You will study a total of 120 credits of taught modules in the first year. Your second year consists of a 60-credit enhanced research project and a 60-credit enhanced dissertation.

The Artificial Intelligence (AI) pathway allows you to graduate with a degree titled 'MSc Computer Science (Artificial Intelligence)'. You will study 30 credits of compulsory AI modules and undertake an AI-focused research project.

Another pathway is offered for those without a computer programming background. This includes compulsory modules in fundamental mathematics and computer science.

Modules

Core

Research Methods

This module will expose you to a variety of research methods, providing you with good quantitative and qualitative skills. Research approaches covered include:

  • laboratory evaluation
  • surveys
  • case studies
  • action research

In addition to project management, the module introduces the research process, from examining how problems are selected, literature reviews, selection of research methods, data collection and analysis, development of theories and conclusions, to the dissemination of the research based on analysis of research papers. The module also offers an overview of ethical considerations when conducting research, and supports in identifying directions for MSc projects.

Students without a background in computer science must take the following:

Programming

This module will give you a comprehensive overview of the principles of programming, including procedural logic, variables, flow control, input and output and the analysis and design of programs. Instruction will be provided in an object-oriented programming language.

Systems and Networks

This module is part of the operating systems and networks theme. The module gives an introduction to the role of the operating system and how it manages computer resources such as memory, processes and disks.

Unix is introduced in terms of the Unix file structure, Input and Output and the Command Line Interface that is used to manipulate these. Computer communication is taught with respect to the Client-Server Architecture and applications that use this. Underlying protocols, such as those in the TCP/IP protocol suite, are introduced, as commonly used on the Internet to provide a universal service. This includes IPv4 and IPv6, the need for IPv6 and how the two differ. Types of computer networks are covered in terms of scale, such as LANs and WANs; and in terms of wired and wireless networks. Mechanisms for connecting networks such as routers, switches and bridges are covered.

Other topics include the role of gateways, proxies, Virtual Private Networks and cloud computing. Potential security risks are examined and how to reduce them, including the use of firewalls.

Databases, Interfaces and Software Design Principles

This module considers both the structure of databases, including how to make them fast, efficient and reliable and the appropriate user interfaces which will make them easy to interact with for users. You will start by looking at how to design a database, gaining an understanding of the standard features that management systems provide and how they can best utilise them, then develop an interactive application to access their database.

Database/software design principles will be introduced with an emphasis on the importance of understanding user requirements and specifications. Throughout the lectures and computing sessions, you will learn how to design and implement systems using a standard database management system, web technologies and GUI interfaces through practical programming/system examples.

Students wishing to obtain MSc Computer Science (Artificial Intelligence) must select 40 credits from the list below:

Autonomous Robotic Systems

This module introduces you to the computer science of robotics, giving you an understanding of the hardware and software principles appropriate for control and localisation of autonomous mobile robots. A significant part of the module is laboratory-based, utilising physical robotic hardware to reinforce the theoretical principles covered.

Spending around three to four hours each week in lectures and practicals, you’ll cover a range of topics including:

  • basic behavioural control architectures
  • programming of multiple behaviours
  • capabilities and limitations of sensors and actuators
  • filtering techniques for robot localisation
Games

This module covers the history, development and state-of-the-art in computer games and technological entertainment.

You will gain an appreciation of the range of gaming applications available and be able to chart their emergence as a prevalent form of entertainment. You will study the fundamental principles of theoretical game design and how these can be applied to a variety of modern computer games.

In addition, you will study the development of games as complex software systems. Specific software design issues to be considered will include the software architecture of games, and the technical issues associated with networked and multiplayer games.

Finally, you will use appropriate software environments to individually develop a number of games to explore relevant theoretical design and practical implementation concepts.

Data Modelling and Analysis

This module will enable you to appreciate the range of data analysis problems that can be modelled computationally and a range of techniques that are suitable to analyse and solve those problems.

Topics covered include:

  • basic statistics
  • types of data
  • data visualisation techniques
  • data modelling
  • data pre-processing methods including data imputation
  • forecasting methods
  • clustering and classification methods (decision trees, naīve bayes classifiers, k-nearest neighbours)
  • data simulation
  • model interpretation techniques to aid decision support

Spending around four hours each week in lectures and computer classes, appropriate software (eg. R, Weka) will be used to illustrate the topics you'll cover.

Fuzzy Logic and Fuzzy Systems

This module aims to provide a thorough understanding of fuzzy sets and systems from a theoretical and practical perspective.

Topics commonly include:

  • type-1 fuzzy sets
  • type-1 fuzzy logic systems
  • type-1 fuzzy set based applications
  • type-2 fuzzy sets
  • type-2 fuzzy logic systems
  • type-2 fuzzy set based applications.

You will also be exposed to some of the cutting-edge research topics in uncertain data and decision making, e.g., based on type-2 fuzzy logic as well as other fuzzy logic representations. You will develop practical systems and software in a suitable programming language.

Simulation and Optimisation for Decision Support

This module offers insight into the applications of selected methods of decision support. The foundations for applying these methods are derived from:

  • operations research simulation
  • social simulation
  • data science
  • automated scheduling
  • decision analysis

Throughout the module, you will become more competent in choosing and implementing the appropriate method for the particular problem at hand. You will engage in a mixture of lectures, workshops, and computer classes.

Linear and Discrete Optimisation

This module provides an entry point to computational optimisation techniques, in particular for modelling and solving linear and discrete optimisation problems like diet optimisation, network flows, task assignment, scheduling, bin-packing, travelling salesmen, facility location, vehicle routing and related problems.

In this module, you will learn to interpret and develop algebraic models for a variety of real-world linear and discrete optimisation problems to then use powerful optimization software (linear, integer and mixed-integer solvers) to produce a solution.

The module covers topics such as:

  • linear programming
  • integer programming
  • combinatorial optimisation
  • modelling and optimisation software
  • multi-objective optimisation 

Optimisation technology is ubiquitous in today's world, for applications in logistics, finance, manufacturing, workforce planning, product selection, healthcare, and any other area where the limited resources must be used efficiently. Optimisation enables prescriptive analytics in order to support and automate decision-making.

Big Data

This module will cover four main concepts.

It will start with an introduction to big data. You’ll find out about the main principles behind distributed/parallel systems with data intensive applications, identifying key challenges such as capture, store, search, analyse and visualise the data. 

We’ll also look at SQL Databases verses NoSQL Databases. You will learn:

  • the growing amounts of data
  • the relational database management systems (RDBMS)
  • an overview of Structured Query Languages (SQL)
  • an introduction to NoSQL databases
  • the difference between a relational DBMS and a NoSQL database
  • how to identify the need to employ a NoSQL database

Another concept is big data frameworks and how to deal with big data. This includes the MapReduce programming model, as well as an overview of recent technologies (Hadoop ecosystem, and Apache Spark). Then, you will learn how to interact with the latest APIs of Apache Spark (RDDs, DataFrames and Datasets) to create distributed programs capable of dealing with big datasets (using Python and/or Scala).  

Finally, we will cover the data mining and machine learning part of the course. This will include data preprocessing approaches, distributed machine learning algorithms and data stream algorithms. To do so, you will use the machine learning library of Apache Spark to understand how some machine learning algorithms can be deployed at a scale. 

Research projects

All students must complete a research project. If you wish to graduate with the title of MSc Computer Science (Artificial Intelligence), you must choose the AI project.

Research Project in Computer Science

You will conduct a piece of empirical and/or theoretical research in an appropriate strand of the degree, under the supervision of a member of academic staff. Where appropriate, your project may also be conducted in conjunction with an external organisation and may involve a substantial software implementation. 

Research Project in Computer Science (Artificial Intelligence)

You will conduct a piece of empirical and/or theoretical research in artificial intelligence, under the supervision of a member of academic staff. Where appropriate, your project may also be conducted in conjunction with an external organisation and may involve a substantial software implementation. 

Optional modules

Data Modelling and Analysis

This module will enable you to appreciate the range of data analysis problems that can be modelled computationally and a range of techniques that are suitable to analyse and solve those problems.

Topics covered include:

  • basic statistics
  • types of data
  • data visualisation techniques
  • data modelling
  • data pre-processing methods including data imputation
  • forecasting methods
  • clustering and classification methods (decision trees, naīve bayes classifiers, k-nearest neighbours)
  • data simulation
  • model interpretation techniques to aid decision support

Spending around four hours each week in lectures and computer classes, appropriate software (eg. R, Weka) will be used to illustrate the topics you'll cover.

Mixed Reality

This module focuses on the possibilities and challenges of interaction beyond the desktop. Exploring the 'mixed reality continuum' - a spectrum of emerging computing applications that runs from virtual reality (in which a user is immersed into a computer-generated virtual world) at one extreme, to ubiquitous computing (in which digital materials appear embedded into the everyday physical world - often referred to as the 'Internet of Things') at the other. In the middle of this continuum lie augmented reality and locative media in which the digital appears to be overlaid upon the physical world in different ways.

You will gain knowledge and hands-on experience of design and development with key technologies along this continuum, including working with both ubiquitous computing based sensor systems and locative media. You will learn about the Human-Computer Interaction challenges that need to be considered when creating mixed reality applications along with strategies for addressing them, so as to create compelling and reliable user experiences.

Programs, Proofs and Types

This module focuses on some of the fundamental mathematical concepts that underlie modern programming and programming languages emphasizing the role of types. We will use a dependently typed programming language/interactive proof system (eg Agda) to implement some concepts on a computer.

Example topics include

  • basic lambda calculus
  • operational semantics
  • domain theory
  • types, propositions as types and formal verification.

You will engage in a mix of lectures and working in the lab with an interactive proof system.

Programming

This module will give you a comprehensive overview of the principles of programming, including procedural logic, variables, flow control, input and output and the analysis and design of programs. Instruction will be provided in an object-oriented programming language.

Advanced Algorithms and Data Structures

You'll study the theory used in the design and analysis of advanced algorithms and data structures. Topics covered include string algorithms (such as for string matching, longest common subsequence), graph algorithms (such as for minimum cuts and maximum flows, and Google's pagerank algorithm), advanced data structures (such as Fibonacci heaps and Bloom filters), and randomised search heuristics (evolutionary algorithms). You'll learn all the necessary probability theory will be introduced, including random variables and concentration inequalities.

The theory is practiced in weekly labs where we learn how to implement the algorithms and data structures as functional and imperative programs (using the languages Haskell and C), and apply these to solve large instances of real-world problems. 

Advanced Computer Networks

This module will provide you with an advanced knowledge of computer communications networks, using examples from all-IP core telecommunications networks to illustrate aspects of transmission coding, error control, media access, internet protocols, routing, presentation coding, services and security.

The module will describe Software Defined Networks (SDNs) and provide examples of using them to enable very large scale complex network control. It will also provide advanced knowledge of various routing and query protocols in:

  • Ad Hoc Networks
  • Mobile Ad Hoc Networks (MANETs)
  • Vehicular Ad Hoc Networks (VANETs)
  • Disconnection/Disruption/Delay Tolerant Networks (DTNs)
  • impact of new networking developments, such as security risks, ethics, interception and data protection will be reflected and discussed systematically
Autonomous Robotic Systems

This module introduces you to the computer science of robotics, giving you an understanding of the hardware and software principles appropriate for control and localisation of autonomous mobile robots. A significant part of the module is laboratory-based, utilising physical robotic hardware to reinforce the theoretical principles covered.

Spending around three to four hours each week in lectures and practicals, you’ll cover a range of topics including:

  • basic behavioural control architectures
  • programming of multiple behaviours
  • capabilities and limitations of sensors and actuators
  • filtering techniques for robot localisation
Systems and Networks

This module is part of the operating systems and networks theme. The module gives an introduction to the role of the operating system and how it manages computer resources such as memory, processes and disks.

Unix is introduced in terms of the Unix file structure, Input and Output and the Command Line Interface that is used to manipulate these. Computer communication is taught with respect to the Client-Server Architecture and applications that use this. Underlying protocols, such as those in the TCP/IP protocol suite, are introduced, as commonly used on the Internet to provide a universal service. This includes IPv4 and IPv6, the need for IPv6 and how the two differ. Types of computer networks are covered in terms of scale, such as LANs and WANs; and in terms of wired and wireless networks. Mechanisms for connecting networks such as routers, switches and bridges are covered.

Other topics include the role of gateways, proxies, Virtual Private Networks and cloud computing. Potential security risks are examined and how to reduce them, including the use of firewalls.

Databases, Interfaces and Software Design Principles

This module considers both the structure of databases, including how to make them fast, efficient and reliable and the appropriate user interfaces which will make them easy to interact with for users. You will start by looking at how to design a database, gaining an understanding of the standard features that management systems provide and how they can best utilise them, then develop an interactive application to access their database.

Database/software design principles will be introduced with an emphasis on the importance of understanding user requirements and specifications. Throughout the lectures and computing sessions, you will learn how to design and implement systems using a standard database management system, web technologies and GUI interfaces through practical programming/system examples.

Project in Advanced Algorithms and Data Structures

This project involves a self-guided study of a selected advanced algorithm or data structure. The outcome of the project is an analysis and implementation of the algorithm or data structure, as well as an empirical evaluation, preferably on a real-world data set of significant size.

Linear and Discrete Optimisation

This module provides an entry point to computational optimisation techniques, in particular for modelling and solving linear and discrete optimisation problems like diet optimisation, network flows, task assignment, scheduling, bin-packing, travelling salesmen, facility location, vehicle routing and related problems.

In this module, you will learn to interpret and develop algebraic models for a variety of real-world linear and discrete optimisation problems to then use powerful optimization software (linear, integer and mixed-integer solvers) to produce a solution.

The module covers topics such as:

  • linear programming
  • integer programming
  • combinatorial optimisation
  • modelling and optimisation software
  • multi-objective optimisation 

Optimisation technology is ubiquitous in today's world, for applications in logistics, finance, manufacturing, workforce planning, product selection, healthcare, and any other area where the limited resources must be used efficiently. Optimisation enables prescriptive analytics in order to support and automate decision-making.

Real-world Functional Programming

This module introduces tools, techniques, and theory needed for programming real-world applications functionally, with a particular emphasis on the inherent benefits of functional programming and strong typing for reuse, maintenance, concurrency, distribution, and high availability. These are all aspects that have contributed to the popularity of functional programming for demanding applications eg in the finance industry and have also had a significant impact on the design of many modern programming languages such as Java, C#, and Rust, and frameworks such as MapReduce and React.

Topics typically include functional design patterns, pure data structures, reactive programming, concurrency, frameworks for web/cloud programming, property-based testing, and embedded domain-specific languages. The medium of instruction is mainly Haskell, but other functional languages, for example, Erlang, may be used where appropriate and for a broader perspective.

If you wish to study some particular topic in scope of this module in more depth, you are encouraged to consider taking the module Real-world Functional Programming Project.

Real World Functional Programming Project MSc

If you choose Real-world Functional Programming, this module allows you to explore a real-world functional programming topic of your choice in depth through a programming project.

Specifically, there are no requirements regarding the programming language used. The language would not even necessarily have to be a functional one. What is important is that the ideas underpinning the project are related to real-world functional programming. You will need to define your project through a project pitch. 

Simulation and Optimisation for Decision Support

This module offers insight into the applications of selected methods of decision support. The foundations for applying these methods are derived from:

  • operations research simulation
  • social simulation
  • data science
  • automated scheduling
  • decision analysis

Throughout the module, you will become more competent in choosing and implementing the appropriate method for the particular problem at hand. You will engage in a mixture of lectures, workshops, and computer classes.

Games

This module covers the history, development and state-of-the-art in computer games and technological entertainment.

You will gain an appreciation of the range of gaming applications available and be able to chart their emergence as a prevalent form of entertainment. You will study the fundamental principles of theoretical game design and how these can be applied to a variety of modern computer games.

In addition, you will study the development of games as complex software systems. Specific software design issues to be considered will include the software architecture of games, and the technical issues associated with networked and multiplayer games.

Finally, you will use appropriate software environments to individually develop a number of games to explore relevant theoretical design and practical implementation concepts.

Fuzzy Logic and Fuzzy Systems

This module aims to provide a thorough understanding of fuzzy sets and systems from a theoretical and practical perspective.

Topics commonly include:

  • type-1 fuzzy sets
  • type-1 fuzzy logic systems
  • type-1 fuzzy set based applications
  • type-2 fuzzy sets
  • type-2 fuzzy logic systems
  • type-2 fuzzy set based applications.

You will also be exposed to some of the cutting-edge research topics in uncertain data and decision making, e.g., based on type-2 fuzzy logic as well as other fuzzy logic representations. You will develop practical systems and software in a suitable programming language.

Malware Analysis

This module looks at the practice of malware analysis, looking at how to analyse malicious software to understand how it works, how to identify it, and how to defeat or eliminate it.  

You will look at how to set up a safe environment in which to analyse malware, as well as exploring both static and dynamic malware analysis. Although malware takes many forms, the focus of this module will primarily be on executable binaries. This will cover object file formats and the use of tools such as debuggers, virtual machines, and disassemblers to explore them. Obfuscation and packing schemes will be discussed, along with various issues related to Windows internals.

The module is practical with encouragement to safely practice the skills you're taught.

Big Data

This module will cover four main concepts.

It will start with an introduction to big data. You’ll find out about the main principles behind distributed/parallel systems with data intensive applications, identifying key challenges such as capture, store, search, analyse and visualise the data. 

We’ll also look at SQL Databases verses NoSQL Databases. You will learn:

  • the growing amounts of data
  • the relational database management systems (RDBMS)
  • an overview of Structured Query Languages (SQL)
  • an introduction to NoSQL databases
  • the difference between a relational DBMS and a NoSQL database
  • how to identify the need to employ a NoSQL database

Another concept is big data frameworks and how to deal with big data. This includes the MapReduce programming model, as well as an overview of recent technologies (Hadoop ecosystem, and Apache Spark). Then, you will learn how to interact with the latest APIs of Apache Spark (RDDs, DataFrames and Datasets) to create distributed programs capable of dealing with big datasets (using Python and/or Scala).  

Finally, we will cover the data mining and machine learning part of the course. This will include data preprocessing approaches, distributed machine learning algorithms and data stream algorithms. To do so, you will use the machine learning library of Apache Spark to understand how some machine learning algorithms can be deployed at a scale. 

The above is a sample of the typical modules we offer but is not intended to be construed and/or relied upon as a definitive list of the modules that will be available in any given year. Modules (including methods of assessment) may change or be updated, or modules may be cancelled, over the duration of the course due to a number of reasons such as curriculum developments or staffing changes. Please refer to the module catalogue for information on available modules. This content was last updated on Friday 11 June 2021.

You can choose to work on a practical project or do a research project. Whichever one you choose, you'll be supported by an academic supervisor who is an active researcher in this area.

Enhanced Masters Research Project in Computer Science

You will complete a significant original research project at the cutting-edge of computer science. Where appropriate, your project may also be done in partnership with an external organisation.

Enhanced Masters Dissertation in Computer Science

This module is a continuation of the Enhanced Masters Research Project in Computer Science module. Building on research carried out in the first semester, you will complete a high-quality dissertation. 

or

Enhanced Masters Research Project Computer Science (Artificial Intelligence)

You will complete a significant original research project at the cutting-edge of artificial intelligence. Where appropriate, your project may also be done in partnership with an external organisation.

Enhanced Dissertation in Computer Science (Artificial Intelligence)

This module is a continuation of the Enhanced Masters Research Project in Computer Science (Artificial Intelligence) module. Building on research carried out in the first semester, you will complete a high-quality dissertation. 

The above is a sample of the typical modules we offer but is not intended to be construed and/or relied upon as a definitive list of the modules that will be available in any given year. Modules (including methods of assessment) may change or be updated, or modules may be cancelled, over the duration of the course due to a number of reasons such as curriculum developments or staffing changes. Please refer to the module catalogue for information on available modules. This content was last updated on Friday 11 June 2021.

Learning and assessment

How you will learn

  • Lectures
  • Tutorials
  • Seminars
  • Computer labs
  • Practical classes
  • Project work
  • Supervision

You will study a total of 120 credits of compulsory and optional taught modules in year one. You will complete a 60-credit research project and a 60-credit project dissertation in year two.

You will work in classrooms and labs to develop a theoretical and practical understanding of this subject.

Teaching is typically delivered by professors, associate and assistant professors. Some practical laboratory sessions and research projects may be supported by postgraduate research students or postdoctoral research fellows.

How you will be assessed

  • Coursework
  • Written exam
  • Project work

Modules are assessed using a variety of individual assessment types which are weighted to calculate your final mark for each module. In many modules, assessments are mixed with 75/25 or 25/75 coursework/exam.

The final degree classification will be the average of all credits, e.g. an average of 120 taught credits and 60 credits on your project. To pass a module you’ll need at least 50%.

Contact time and study hours

The class size depends on the module. In 2019/2020 class sizes ranged from 25 to 110 students.

All students meet their tutors in the Induction week. Students are then encouraged to make individual arrangements to discuss any issues during the study. Some staff offer weekly drop-in time for students.

Entry requirements

All candidates are considered on an individual basis and we accept a broad range of qualifications. The entrance requirements below apply to 2021 entry.

Undergraduate degree2:1 (or international equivalent) with evidence of an interest or aptitude for programming; graduates from a science or engineering background will be considered with a 55% average mark

Applying

International applicants must apply by Sunday 07 February 2021

You may also find our international postgraduate fees page useful.

Our step-by-step guide covers everything you need to know about applying.

How to apply

Fees

Qualification MSc year one MSc year two
Home / UK £5,667 The fee for year two will be 50% of the year one fee applicable in the year that year two is taken.
International £16,667 The fee for year two will be 50% of the year one fee applicable in the year that year two is taken.

If you are a student from the EU, EEA or Switzerland starting your course in the 2021/22 academic year, you will pay international tuition fees.

This does not apply to Irish students, who will be charged tuition fees at the same rate as UK students. UK nationals living in the EU, EEA and Switzerland will also continue to be eligible for ‘home’ fee status at UK universities until 31 December 2027.

For further guidance, check our Brexit information for future students.

Additional costs

We do not anticipate any extra significant costs. You should be able to access most of the books you’ll need through our libraries, though you may wish to purchase your own copies which you would need to factor into your budget.

Funding

To help support our students, we offer an Excellence in Computer Science scholarship. There are three levels to the award which range from 10-50% off your tuition fees. You will also receive a tablet to use for the duration of your study.

There are many ways to fund your postgraduate course, from scholarships to government loans.

We also offer a range of international masters scholarships for high-achieving international scholars who can put their Nottingham degree to great use in their careers.

Check our guide to find out more about funding your postgraduate degree.

Postgraduate funding

Careers

We offer individual careers support for all postgraduate students.

Expert staff can help you research career options and job vacancies, build your CV or résumé, develop your interview skills and meet employers.

More than 1,500 employers advertise graduate jobs and internships through our online vacancy service. We host regular careers fairs, including specialist fairs for different sectors.

Graduate destinations

This course prepares you for careers in advanced software development, particularly where reliability and efficiency are vital requirements. Graduates are likely to assume leading roles in major software-development projects in one of the areas of specialisation.

This course also provides an excellent foundation for further study and you may decide to progress to a PhD in order to continue your research.

Our graduates have lots of great job opportunities. Computer science-related skills make up 4 of the top 5 'most in-demand skills for employers in 2020’ according to LinkedIn.

Career progression

96.4% of undergraduates from the School of Computer Science secured graduate level employment or further study within 15 months of graduation. The average annual salary for these graduates was £28,895.*

* HESA Graduate Outcomes 2020. The Graduate Outcomes % is derived using The Guardian University Guide methodology. The average annual salary is based on graduates working full-time within the UK.

Two masters graduates proudly holding their certificates
" I'm an Associate Professor in the School of Computer Science. I teach modules related to artificial intelligence including Fundamentals of AI and Topics in AI. My main research interests include artificial intelligence algorithms (eg computational optimisation algorithms with machine learning) for intelligent transport systems (eg vehicle routing and connected vehicles) and optimisation problems in workforce scheduling, telecommunication network routing and timetabling. "
Rong Qu, School of Computer Science

Related courses

The University has been awarded Gold for outstanding teaching and learning (2017/18). Our teaching is of the highest quality found in the UK.

The Teaching Excellence Framework (TEF) is a national grading system, introduced by the government in England. It assesses the quality of teaching at universities and how well they ensure excellent outcomes for their students in terms of graduate-level employment or further study.

This content was last updated on Friday 11 June 2021. Every effort has been made to ensure that this information is accurate, but changes are likely to occur given the interval between the date of publishing and course start date. It is therefore very important to check this website for any updates before you apply.