- Entry requirements: Related 2:1 degree (or equivalent)
- Full-time: 24 months
Gain hands-on experience of big data analytics, data mining and visualisation techniques using high-performance computer technology. This MSc is accredited by BCS, the Chartered Institute for IT, and includes an extended industrial placement in a real-world environment.
Big data is increasingly important in the contemporary business and IT world. For many public and private enterprises, analysis of large-scale data sets is critical to growth. This MSc will prepare you for employment in an IT industry where big data professionals are in high demand.
You’ll learn how to interrogate vast amounts of data and make informed insights from datasets that are too large to be readily processed using standard techniques.
In year one, we’ll provide an overview of the key algorithms, algorithmic approaches and software environments you’ll use when solving big data problems and explore data mining techniques. We’ll also guide you in how to plan and conduct research.
Hands-on programming experience with the latest multi-core and multi-processor platforms will ensure your expertise in big data is underpinned by knowledge of high-performance computing. Further opportunities to specialise and enhance your knowledge of algorithms, optimisation and machine learning are available through a range of optional modules.
In year two, you’ll undertake an industrial project in a real-world environment as part of an extended placement opportunity. While on placement, you’ll develop transferable skills and gain insight into the operations, products, practices and culture of the placement provider.
The programme is accredited by BCS, The Chartered Institute for IT, the leading professional body for those working in IT. It is continually updated to reflect new technologies and trends.
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.
This is a module to cover theoretical and practical aspects of parallel programming for multi-core architectures with the main focus on hand-on programming experience with latest multi-core and multi-processor platforms.
This module provides an initial overview of key algorithms and algorithmic approaches and corresponding software environments used when developing solutions to Big Data problems and explains how to use these to analyse data. A significant portion of statistics, some advanced AI approaches as well as key deterministic and hybrid algorithms are included to support the development of future data analytics and to understand how to develop stochastic, machine learning and hybrid algorithms that can exploit Big Data and can be applied to solve real life problems.
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 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.
Masters module on practical algorithms and data structures for large datasets.
This module is an in-depth tour over optimisation methods applied for various optimisation models. These methods are extensively used in both academic and industrial practices.
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.
Safety and Dependability will cover techniques for the validation of systems against formal specifications. In a first part, safety specifications (something bad never happens) using the Hoare calculus and safe abstraction are covered. A second part refers to termination (something good eventually happens), exploiting well foundedness. In a third part, Markov chains and decision processes are studied, extending the qualitative safety and termination problems from the first part to qualitative/probabilistic properties, and extending them to a simple probabilistic specification language, PCTL. As part of the module, the ability of formulating (probabilistic) models as Markov chains and decision processes are taught, as well as the use of of-the-shelf tools like PRISM or IscasMC for their analysis.
The module introduces the student to the use of logic as a tool for specifying the desired behavior of hardware, software and artificial intelligence systems, and for checking whether a given system does indeed behave as desired. The module enables the student to gain familiarity with a set of techniques which are critical in contemporary industrial applications and in academic research. It consists of 30 lectures and 10 practical sessions.
This module focuses on algorithmic aspects of game theory. A main focus of this module is on the computational aspects in the design of mechanisms and auctions. as part of the module, the students learn about Googles sponsored serarch auctions, which is one of the most successful targeted advertising systems today.
This module is a major part of the 2 year MSc programmes with a year in industry. It is worth 60 credits. The project takes place in the year 2 during the placement period, typically from September to the following May for a minimum of 26 weeks. An earlier starting date is allowed, but subject to agreement between the University and the placement provider. The module will be assessed by means of: An interim report, An oral presentation, and A final dissertation. The student will be supervised by a university academic as well as an industrial supervisor. This module is aimed at developing the student’s ability to undertake an industrial project in a real world environment successfully and with limited supervision. The student is expected to apply the knowledge acquired from the taught components of the programme and to gain significant knowledge and skills in industry. As a consequence, the expectation is that students’ employability prospects will be greatly enhanced.
This module contains the elements of the Computer Science PGT programmes with a second year industrial placement for the 2 year MSc programmes that are generic to all underlying work experience. The technical elements of the placement are contained in a different module, which is COMP599 MSc Industrial Project.
Teaching on the first year of this programme comprises formal lectures, small group tutorials and practical sessions in PC and Mac laboratories. You will also take part in one or more group projects. In your second year, you’ll undertake an industrial project in a real-world environment.
Modules in the first year of the course 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.
The second year of the course is assessed through a portfolio of evidence from your industrial placement and a major project undertaken in your placement setting.
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:
Want to find out more about student life?
Chat with our student ambassadors and ask any questions you have.
The programme is accredited by BCS, The Chartered Institute for IT, for the purposes of partially meeting the academic requirement for registration as a Chartered IT Professional.
Designed to address a skills gap in the employment market, this MSc will enable you to apply your skills working with big data and your knowledge of high performance computing to real-world challenges.
The MSc has been developed, and is delivered, in close collaboration with the Hartree Centre in Daresbury, near Warrington. Hartree operate the UK’s largest supercomputer, capable of a thousand trillion calculations per second. They collaborate with both industry and the research community to help UK businesses and organisations explore and adopt supercomputing, data science and artificial intelligence technologies.
Potential roles working with big data include:
The transferable skills you develop will also prepare you for a variety of other roles across the IT industry, while your expertise working with data will mean you’re well suited to 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|
|Year in industry fee||£2,450|
|Full-time place, per year||£26,350|
|Year in industry fee||£5,300|
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 2:1 honours degree, or above, or equivalent. This degree should be in computer science or a closely related subject.
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|
Discover more about the city and University.
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.
Professor Igor Potapov
Last updated 23 March 2023 / / Programme terms and conditions /