Discover how tech companies gather and use data and explore both the databases that power our daily lives and the data languages underpinning them on this MSc. You’ll receive a thorough grounding in mathematics and statistics, data mining, artificial intelligence and the fundamentals of programming as you develop a toolkit of skills in data science and emerging technologies.
Introduction
With organisations increasingly reliant on data science and artificial intelligence (AI), understanding how to analyse, validate and interpret data can significantly enhance your employability.
If you’re a graduate in a subject that’s not related to computer science, this MSc will complement your first degree and prepare you to meet the high demand for professionals in data science and AI technologies.
With the roots of data science embedded in mathematics, we’ll start by introducing you to linear algebra, differential calculus, probability theory and statistics. This will prepare you for working with data mining algorithms and experimenting using real-world data.
You’ll receive an overview of how to design and create software, including an intensive introduction to programming, and explore key topics in AI. We’ll also provide a thorough grounding in how to plan and conduct research in preparation for your dissertation.
Further opportunities to specialise and enhance your knowledge of big data, web programming, bio-inspired algorithms and modern information systems are available through a range of optional modules.
Who is this course for?
This MSc is suitable if your first degree was in a subject not related to computer science and you are seeking career opportunities in data science and artificial intelligence.
What you'll learn
How to design and create software using a modern programming language
Essential mathematical concepts and techniques in linear algebra, differential calculus, probability theory and statistics
Key topics in artificial intelligence, including machine learning, deep learning, natural language processing (NLP) and computer vision
How to develop web applications
The role of artificial intelligence in communication
Data mining techniques and challenges using real-world datasets
Application of visualisation methods to data mining
Research skills in computer science
Bio-inspired algorithms for optimisation and machine learning
How big data is collected and used in modern society
The role of structured query language, SQL, in information systems.
Accreditation
This course is pending accreditation by BCS, The Chartered Institute for IT.
Course content
Discover what you'll learn, what you'll study, and how you'll be taught and assessed.
September start
Compulsory modules
Research Methods in Computer Science (COMP516)
Credits: 15 /
Semester: semester 1
In this module the students will learn and practise all the necessary skills needed to conduct independent research in computer science, including literature search, project management, presentation techniques, peer reviewing, writing skills and critical review of texts. They will also learn about the professional, legal, social and ethical framework of the IT industry. The module covers, e.g., planning and scheduling projects and drawing Gantt charts. Students shall also conduct a research project (including research, paper, literature review, or MSc project proposal, …) and use tools like EndNote and Zotero bibliography manager within MS Word and Latex.
Programming Fundamentals (COMP517)
Credits: 15 /
Semester: semester 1
The aim of COMP517 is to help you to learn how to design and create software. Central to this will be an understanding of and practical experience with a modern programming language, but you will also be made aware of the importance of using sound software engineering techniques to develop high quality programs. As with many endeavours (swimming, chess-playing, story-writing) programming is a skill that must be learned and improved upon by constant practice. In this module, therefore, the emphasis will be on self-study. Although lectures will be used to introduce the various topics, you will be expected to spend the majority of your time attempting numerous exercises and completing the specified assignments.
Maths and Statistics for AI and Data Science (COMP533)
Credits: 15 /
Semester: semester 1
Computer Science in general, and data Science in particular, has its roots in Mathematics. This module is designed to bring you up to speed with the necessary mathematical and statistical underpinning required to study Data Science and AI.
Applied Artificial Intelligence (COMP534)
Credits: 15 /
Semester: semester 2
This module gives an introduction to key areas of Artificial Intelligence (AI), including Machine Learning, Deep Learning, Natural Language Processing (NLP) and Computer Vision.
It discusses fundamental problems in these areas and covers common methods to solve them.
Students will develop the practical skills necessary to build AI applications using data from different domains.
MSc Project (COMP702)
Credits: 60 /
Semester: summer
Masters Level final project (individual project with dissertation)
Optional modules
Database and Information Systems (COMP518)
Credits: 15 /
Semester: semester 1
This module focuses on how databases are used in modern information systems. They are at the heart of almost all systems, such as supermarket checkouts, online banking, home rentals, and much more. One of the most successful data definition and manipulation languages is SQL, which will be covered in detail. The module will also introduce some of the fundamental concepts in computer science, as well as the mathematical underpinnings of relational databases and the techniques used to support concurrency and reliability in large information systems.
Big Data and Society: Foundations, Politics, and Policy B (COMM752)
Credits: 15 /
Semester: semester 1
This module will be of particular interest to students interested in big data and how it is collected and used in modern society; in the politics and policy questions around social media; and in the interactions between media, platforms, and citizens. It will introduce students to the study of online media and platforms, with a particular focus on ‘big’ social trace data. As well as developing their understanding of how Internet-based media systems work, students will learn about the strengths and weaknesses of using big data for social science research, and engage with key online political communication policy questions.
Data Mining and Visualisation (COMP527)
Credits: 15 /
Semester: semester 2
The module covers a range of topics and techniques for analyzing data. Students will learn about different types of data mining problems, including classification, clustering, association pattern mining, and social network analysis, as well as algorithms to solve them.
Students will program selected data mining algorithms from scratch using Python. This hands-on approach will allow them to gain a deeper understanding of how the algorithms work and how they can be applied to real-world datasets. They will experiment with different datasets to see how the algorithms perform and learn how to interpret the results.
Web Programming (COMP519)
Credits: 15 /
Semester: semester 2
Masters level introductory web programming module covering such topics as HTML, Cascading Style Sheets, CGI programming, and PHP/SQL programming.
MSc Group Project (COMP530)
Credits: 15 /
Semester: semester 2
This module is designed to allow students to consolidate work from the first semester by working as a programming team to realise a solution to a problem related to their programme of study.
Machine Learning and BioInspired Optimisation (COMP532)
Credits: 15 /
Semester: semester 2
This module teaches you about bio-inspired algorithms for optimisation and machine learning. The algorithms are based on reinforcement learning, DNA computing, brain or neural network models, immune systems, the evolutionary version of game theory, and social insect swarm behaviour such as ant colonies and bee colonies. These techniques are extremely useful for searching very large solution spaces (optimisation) and they can be used to design agents or robots that have to interact and operate in dynamic unknown environments (e.g. a Mars rover, a swarm of robots or network of satellites). The idea of learning optimal behaviour, rather than designing, algorithms and controllers is especially appealing in AI.
Computational Intelligence (COMP575)
Credits: 15 /
Semester: semester 2
Biologically inspired optimisation and introduction to neural networks for artificial intelligence.
Artificial Intelligence and Communication B (COMM718)
Credits: 15 /
Semester: semester 2
This module will provide students with skills to understand, analyse and master the role played by Artificial Intelligence in Communication. It will introduce students to core notions to identify what components of our daily communication practices are affected by AI, how the reshaping of the communication processes happens through different technologies and how we can check their evolutions being aware of their potential risks and opportunities. At the end of the module students will be able to answer questions such as: who are we communicating with when we write online? How are (chat)bots and conversational agents changing our interactions? Why social and new digital media are affecting news consumption habits? The module will be taught following "active learning" methodologies.
Big Data and Society: Algorithms and Platforms B (COMM754)
Credits: 15 /
Semester: semester 2
In addition to learning about the algorithms that influence the development of online social systems, students will critically address key questions around the political and economic consequences of online platforms. The course emphasises a hands-on approach to studying algorithms in practice, developing students’ programming skills to implement and explore their effects.
Any optional modules listed above are illustrative only and may vary from year to year. Modules may be subject to minimum student numbers being achieved and staff availability. This means that the availability of specific optional modules cannot be guaranteed.
January start
Compulsory modules
Research Methods in Computer Science (COMP516)
Credits: 15 /
Semester: semester 1
In this module the students will learn and practise all the necessary skills needed to conduct independent research in computer science, including literature search, project management, presentation techniques, peer reviewing, writing skills and critical review of texts. They will also learn about the professional, legal, social and ethical framework of the IT industry. The module covers, e.g., planning and scheduling projects and drawing Gantt charts. Students shall also conduct a research project (including research, paper, literature review, or MSc project proposal, …) and use tools like EndNote and Zotero bibliography manager within MS Word and Latex.
Programming Fundamentals (COMP517)
Credits: 15 /
Semester: semester 1
The aim of COMP517 is to help you to learn how to design and create software. Central to this will be an understanding of and practical experience with a modern programming language, but you will also be made aware of the importance of using sound software engineering techniques to develop high quality programs. As with many endeavours (swimming, chess-playing, story-writing) programming is a skill that must be learned and improved upon by constant practice. In this module, therefore, the emphasis will be on self-study. Although lectures will be used to introduce the various topics, you will be expected to spend the majority of your time attempting numerous exercises and completing the specified assignments.
Maths and Statistics for AI and Data Science (COMP533)
Credits: 15 /
Semester: semester 1
Computer Science in general, and data Science in particular, has its roots in Mathematics. This module is designed to bring you up to speed with the necessary mathematical and statistical underpinning required to study Data Science and AI.
MSc Project (COMP702)
Credits: 60 /
Semester: summer
Masters Level final project (individual project with dissertation)
Applied Artificial Intelligence (COMP534)
Credits: 15 /
Semester: semester 2
This module gives an introduction to key areas of Artificial Intelligence (AI), including Machine Learning, Deep Learning, Natural Language Processing (NLP) and Computer Vision.
It discusses fundamental problems in these areas and covers common methods to solve them.
Students will develop the practical skills necessary to build AI applications using data from different domains.
Optional modules
Artificial Intelligence and Communication B (COMM718)
Credits: 15 /
Semester: semester 2
This module will provide students with skills to understand, analyse and master the role played by Artificial Intelligence in Communication. It will introduce students to core notions to identify what components of our daily communication practices are affected by AI, how the reshaping of the communication processes happens through different technologies and how we can check their evolutions being aware of their potential risks and opportunities. At the end of the module students will be able to answer questions such as: who are we communicating with when we write online? How are (chat)bots and conversational agents changing our interactions? Why social and new digital media are affecting news consumption habits? The module will be taught following "active learning" methodologies.
MSc Group Project (COMP530)
Credits: 15 /
Semester: semester 2
This module is designed to allow students to consolidate work from the first semester by working as a programming team to realise a solution to a problem related to their programme of study.
Web Mapping and Geovisualisation (ENVS456)
Credits: 15 /
Semester: semester 2
Through this module students will gain an understanding of how maps can be visualised online through a number of web platforms. Additionally, the internet will be presented both as a source of new data, and provide analytical functionality that can assist when solving geographic problems. Geographic data can be any dataset that can be visualised in a map. The module is taught through a mixture of lectures and practicals, and is assessed through two summative projects.
Database and Information Systems (COMP518)
Credits: 15 /
Semester: semester 1
This module focuses on how databases are used in modern information systems. They are at the heart of almost all systems, such as supermarket checkouts, online banking, home rentals, and much more. One of the most successful data definition and manipulation languages is SQL, which will be covered in detail. The module will also introduce some of the fundamental concepts in computer science, as well as the mathematical underpinnings of relational databases and the techniques used to support concurrency and reliability in large information systems.
Big Data and Society: Foundations, Politics, and Policy B (COMM752)
Credits: 15 /
Semester: semester 1
This module will be of particular interest to students interested in big data and how it is collected and used in modern society; in the politics and policy questions around social media; and in the interactions between media, platforms, and citizens. It will introduce students to the study of online media and platforms, with a particular focus on ‘big’ social trace data. As well as developing their understanding of how Internet-based media systems work, students will learn about the strengths and weaknesses of using big data for social science research, and engage with key online political communication policy questions.
Biocomputation (COMP305)
Credits: 15 /
Semester: semester 1
Biology inspired adaptive algorithms such as Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) play an important role in modern computing, information processing, and machine learning. The latest increase in computer power ensured broad use of the algorithms to solve problems in science and engineering previously considered impossible to tackle. ANNs are now broadly used in pattern recognition, including speech recognition and classification problems, statistics, functional analysis, modelling financial series with considerable stochasticity, etc. GAs are search procedures based on the mechanics of natural selection and natural genetics. They provide effective solutions to a variety of optimisation problems in economics, linguistics, engineering, and computer science. Both ANNs and GAs can exploit massively parallel architectures to speed up problem solving and provide further understanding of intelligence and adaptation.The main goals of the module are to introduce students to some of the established work in the field of Artificial Neural Networks and Genetic Algorithms and their applications, particularly in relation to multidisciplinary research. To equip students with a broad overview of the field, placing it in a historical and scientific context. The module provides students with the knowledge and skills necessary to keep up-to-date in actively developing areas of science and technology and be able to make reasoned decisions.
Geographic Data Science (ENVS563)
Credits: 15 /
Semester: semester 1
This module will introduce students to the nascent field of Geographic Data Science (GDS), a discipline established at the intersection between Geographic Information Science (GIS) and Data Science. The course covers how the modern GIS toolkit can be integrated with Data Science tools to solve practical real-world problems. Core to the set of employable skills to be taught in this course is an introduction to programming tools for GDS in R and Python. The programme of lectures, guided practical classes and independent study illustrate how and why GDS is useful for social science applications.
Any optional modules listed above are illustrative only and may vary from year to year. Modules may be subject to minimum student numbers being achieved and staff availability. This means that the availability of specific optional modules cannot be guaranteed.
How you'll learn
Teaching on this programme comprises formal lectures, small group tutorials and practical sessions in computer laboratories. You will also take part in one or more group projects. At the end of the year, you’ll complete a major individual research project under expert supervision.
How you're assessed
Modules are assessed through a combination of examinations and coursework. The examinations take place at the end of each semester and typically take the form of an in-person written assignment, usually to be completed in a couple of hours. You’ll be assigned coursework across the length of each semester. This typically takes the form of class tests, programming assignments or small projects.
Your dissertation is assessed through a combination of written reports and a presentation of your achievements.
Liverpool Hallmarks
We have a distinctive approach to education, the Liverpool Curriculum Framework, which focuses on research-connected teaching, active learning, and authentic assessment to ensure our students graduate as digitally fluent and confident global citizens.
Course options
Studying with us means you can tailor your degree to suit you. Here's what is available on this course.
The Department of Computer Science is housed in a grade II listed building which has been extensively refurbished for 21st century needs and challenges and provides state-of-the art equipment and high-speed communication links.
A tour of the Computer Science department and facilities
Dr Terry Payne talks you through what you can expect studying Computer Science at the University of Liverpool and shows you some of the facilities and equipment you will be using.
Virtual tour
Supporting your learning
From arrival to alumni, we’re with you all the way:
Careers and employability support, including help with career planning, understanding the job market and strengthening your networking skills
A dedicated student services team can help you get assistance with your studies, help with health and wellbeing, and access to financial advice
Support for students with differing needs from the Disability advice and guidance team. They can identify and recommend appropriate support provisions for you.
An exciting place to study Computer Science
You will be taught by some of the best researchers in the field. In the most recent Research Excellence Framework, the research output of our department was ranked 5th in the UK. This research expertise shows through in our teaching
We teach in state-of-the-art PC and Mac laboratories running a variety of different operating systems, as well as iOS and Android tablets to encourage creativity and innovation within a stimulating environment in which to work and study
Our programmes are continually updated to reflect new technologies and trends.
What students say...
I like the curriculum, which is super friendly for students who may not have a strong computer science background. Learning data science and artificial intelligence at a top-ranked university for computer sciences is a coveted opportunity for me.
A day in the life of Computer Science student Shania
Careers and employability
Data science and artificial intelligence driven technologies are becoming integral parts of our lives and changing the ways people do business.
Nearly every organisation uses data science and artificial intelligence to refine and streamline their business practices. The significant opportunities afforded by the application of data science and artificial intelligence across so many different sectors, from IT and healthcare to government agencies, mean that professionals in this area are in high demand, with job opportunities far outstripping supply.
This MSc addresses this skills gap by preparing you for an exciting career in data science and artificial intelligence. This includes interdisciplinary opportunities tailored to your individual expertise, achieved by coupling knowledge of data science and artificial intelligence with the knowledge that you acquired from your first degree.
Career support from day one to graduation and beyond
87% of Computer Science students find their main activity after graduation meaningful.
Graduate Outcomes, 2018-19.
Fees and funding
Your tuition fees, funding your studies, and other costs to consider.
Tuition fees
UK fees (applies to Channel Islands, Isle of Man and Republic of Ireland)
Full-time place, per year
£13,300
International fees
Full-time place, per year
£30,800
Fees stated are for the 2025-26 academic year.
Tuition fees cover the cost of your teaching and assessment, operating facilities such as libraries, IT equipment, and access to academic and personal support.
If you're a UK national, or have settled status in the UK, you may be eligible to apply for a Postgraduate Loan worth up to £12,167 to help with course fees and living costs. Learn more about fees and funding.
Additional costs
We understand that budgeting for your time at university is important, and we want to make sure you understand any course-related costs that are not covered by your tuition fee. This could include buying a laptop, books, or stationery.
We accept a 2:2 honours degree from a UK university, or an equivalent academic qualification from a similar non-UK institution. This degree should be in a subject that’s not related to computer science.
International qualifications
If you hold a bachelor’s degree or equivalent, but don’t meet our entry requirements, you could be eligible for a Pre-Master’s course. This is offered on campus at the University of Liverpool International College, in partnership with Kaplan International Pathways. It’s a specialist preparation course for postgraduate study, and when you pass the Pre-Master’s at the required level with good attendance, you’re guaranteed entry to a University of Liverpool master’s degree.
International applicants who do not meet the minimum required standard of English language can complete one of our Pre-Sessional English courses to achieve the required level.
English language qualification
Requirements
IELTS
6.5 overall, with no component below 5.5
TOEFL iBT
88 overall, with minimum scores of listening 17, writing 17, reading 17 and speaking 19. TOEFL Home Edition not accepted.
Duolingo English Test
120 overall, with no component below 95
Pearson PTE Academic
61 overall, with no component below 59
LanguageCert Academic
70 overall, with no skill below 60
PSI Skills for English
B2 Pass with Merit overall and no band below B2 Pass
INDIA Standard XII
National Curriculum (CBSE/ISC) - 75% and above in English. Accepted State Boards - 80% and above in English.
WAEC
C6 or above
PRE-SESSIONAL ENGLISH
Do you need to complete a Pre-Sessional English course to meet the English language requirements for this course?
The length of Pre-Sessional English course you’ll need to take depends on your current level of English language ability.
Liverpool bursts with diversity and creativity which makes it ideal for you to undertake your postgraduate studies and access various opportunities for you and your family.
To fully immerse yourself in the university experience living in halls will keep you close to campus where you can always meet new people. Find your home away from home.
Discover what expenses are covered by the cost of your tuition fees and other finance-related information you may need regarding your studies at Liverpool.
Contact us
Have a question about this course or studying with us? Our dedicated enquiries team can help.
