- Entry requirements: A high 2:1 degree (or equivalent)
- Full-time: 12 months
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.
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.
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.
This course is pending accreditation by BCS, The Chartered Institute for IT.
Discover what you'll learn, what you'll study, and how you'll be taught and assessed.
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.
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 in reading the corresponding textbook chapters, attempting numerous exercises, and completing the specified assignments.
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.
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.
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.
To provide an in-depth, systematic and critical understanding of some of the current research issues at the forefront of the academic research domain of data mining. As part of the module students program with Python selected data mining algorithms and experiment using real-world datasets. Google search framework and IBM Watson QA system and various other industrial level data mining applications are discussed.
Skills: Communication skills (listening andquestioning, respecting others, contributing to discussions, communicating in a foreign language, presenting own work in form of a talk) This skill is not evaluated in the module. However, students are encouraged to verbally participate in the numerous in-class quizzes about data mining concepts.
Two Python programming assignments (accounting for 25% of the total mark for the module) arecirculated. The students are expected to implement a selected group of data mining algorithms from the scratch by themselves and experiment using real-world datasets.
Business and customer awareness (basic understanding of the key drivers for business success – including the importance of innovationand taking calculated risks – and the need to provide customer satisfaction and build customer loyalty) Google search framework, IBM Watson QA system and various other industrial level data mining applications are discussed in the class as specific implementations of the algorithms introduced in the module.
Information Technology (IT) skills (IT skills, including familiarity with word processing, spreadsheets, file management, use of internet search engines, use of specific software and/or IT and programming paradigms) Students are required to use industry-level data processing libraries such as numeric python library, scientific python library and scikit-learn machine learning library during the lab sessions.
Computer science principles
Examples: Formal tools for building and verifying complex electronic-commerce systems (name some concrete software). Formal methods for deriving classification algorithms that focus on different loss functions such as the cross-entropy loss (logistic regression), hinge loss (support vector machines) are taught in the module.
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.
Masters level introductory web programming module covering such topics as HTML, Cascading Style Sheets, CGI programming, and PHP/SQL programming.
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.
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.
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.
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.
Biologically inspired optimisation and introduction to neural networks for artificial intelligence.
Masters Level final project (individual project with dissertation)
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.
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.
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.
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.
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.
From arrival to alumni, we’re with you all the way:
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.
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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.
You’ll be well placed on graduation to secure a variety of roles, including:
Your expertise working with data will also provide ideal preparation for potential PhD study.
Your tuition fees, funding your studies, and other costs to consider.
|UK fees (applies to Channel Islands, Isle of Man and Republic of Ireland)|
|Full-time place, per year||£12,100|
|Full-time place, per year||£26,350|
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 tuition fees, funding and Postgraduate Loans.
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.
Find out more about the additional study costs that may apply to this course.
We offer a range of scholarships and bursaries to help cover tuition fees and help with living expenses while at university.
The qualifications and exam results you'll need to apply for this course.
My qualifications are from: United Kingdom.
|Postgraduate entry requirements||
You will normally need a high 2:1 honours degree (65%), or above, or equivalent. This degree should be in a subject that’s not related to computer science.
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.
You'll need to demonstrate competence in the use of English language. 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|
View our IELTS academic requirements key.
Standard Level 5
|TOEFL iBT||88 or above with minimum scores in components as follows: Listening and Writing 17, Reading 17, Speaking 19.|
|INDIA Standard XII||70% or above from Central and Metro State Boards|
|Hong Kong use of English AS level||C|
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Last updated 4 May 2023 / / Programme terms and conditions /