Overview
Computer Science at Brunel is ranked 4th in London by the Times Higher Education World Rankings 2024.
From social networks, ecommerce and government through to sensors, smart meters and mobile networks, data is being collected at an unprecedented speed and scale. But big data is of little use without 'big insight'. The skills required to develop such insight are in short supply and the shortage of skilled workers in the data analytics market is cited as a key barrier to unlocking everything that data has to offer.
The Data Science and Analytics MSc programme provides these skills, combining a strong academic degree course with hands-on experience of leading commercial technology, and the chance to gain industry certification. You will develop both your critical awareness of the very latest developments in data science and the practical skills that help you apply data science more effectively in a wide variety of sectors including finance, retail and government.
You’ll gain knowledge of key concepts and the nuances of effective data analysis. You’ll gain confidence in your own critical understanding of the challenges and issues arising from taking heterogeneous data at volume and scale, understanding what it represents, and turning that understanding into insight for business, scientific or social innovation. You’ll develop a practical understanding of the skills, tools and techniques necessary for the effective application of data science.
The course is designed to offer you the opportunity to gain hands-on experience in several data analytics tools (e.g. Hadoop, Spark, Tableau), programming languages (R, Python) and machine learning libraries. A series of sessions in Python is offered to support students who are less familiar with programming. You will also have the opportunity to obtain an SAS certificate such as SAS Base Programming, which is a recognised industry qualification, following a two-week SAS certification ‘boot camp’.
If you don’t want to commit to full or part-time study of the entire MSc, you can develop your educational portfolio over a longer period of time by undertaking staged study that leads to the award of Postgraduate Certificate (PGCert in Data Science), Postgraduate Diploma (PGDip in Data Science and Analytics) and Data Science and Analytics MSc in separate stages.
The roles that our graduates are typically recruited to within these organisations include analytics consultant, big data engineer/scientist, business analyst, clinical data scientist, data design specialist, data scientists, developer/development engineer, enterprise/technical architect, forecast analyst, marketing/customer and/or insight analyst, quantitative analyst and web analyst. Brunel’s Women in Engineering and Computing mentoring scheme provides our female students with invaluable support from their industry mentors.
International accolades for computer science at Brunel
You will join a truly international and diverse community at Brunel University of London which has recently been ranked joint first in the UK and sixth in the world for ‘International Outlook’ by the Times Higher Education World University Rankings 2025. You will be taught by globally recognised experts in the computer science field, with eight academics in the department ranked among the top 2% of scientists globally (Stanford and Elsevier rankings 2024). Our department is also ranked ninth in the UK, and within the top 101-150 worldwide by the prestigious Shanghai global ranking of academic subjects 2024.
You can explore our campus and facilities for yourself by taking our virtual tour.
Course content
This incredibly relevant and current course will equip you with all the skills you need to venture out into the world of analytics and big data.
Compulsory
- Data VisualisationThe aim of this module is to develop the reflective and practical understanding necessary to visually present insight drawn from large heterogeneous data sets to, for example, decision makers. Content will provide an understanding of human visual perception, data visualisation methods and techniques, dashboard and infographic design. The role of interactivity within the visualisation process will be explored and an emphasis placed on visual storytelling and narrative development.
- Research Project ManagementThis module aims to develop and deploy the skills necessary to design a scholarly piece of research work to address an identified problem area within the chosen field of study.
- Ethics and Governance of Digital Systems
This module aims to develop a critical understanding of topics related to the handling and governance of digital information in contemporary systems contexts. Such topics will include the way that networked and intelligent systems are designed and used; the motivations for their adoption; the substantive issues arising; and approaches to their regulation and governance. Examples from the public and private sectors will be used to illustrate these developments.
- Digital Innovation and StrategyThe aim of this module is to develop knowledge and skills necessary for the implementation of digital business models and technologies intended to realign an organisation with the changing demands of its business environment (or to capitalise on business opportunities).
- Machine LearningThe aim of this module is to develop the reflective and practical understanding necessary to extract value and insight from heterogeneous data sets using statistical learning. Focus is placed on the analytic methods/techniques/algorithms for generating value and insight from the processing of heterogeneous data. Content will cover machine learning techniques, such as principal component analysis, cluster analysis, decision trees and random forest, support vector machines, as well as approaches to performance evaluation.
- High Performance Computational InfrastructuresThis module aims to develop knowledge and skills necessary for working effectively with the large-scale data storage and processing infrastructures that underpin data science. You will develop both practical skills and an ability to reflect critically on concepts, theory and appropriate use of infrastructure. Content covers highly scalable cloud computing tools, for example Hadoop, and in-memory approaches, such as Spark.
- Quantitative Data AnalysisThe aim of this module is to develop knowledge and skills of the quantitative data analysis methods that underpin data science. Content covers a practical understanding of core methods in data science application and research, such as bivariate and multivariate methods, regression and graphical models. A focus is also placed on learning to evaluate the strengths and weaknesses of methods alongside an understanding of how and when to use or combine methods.
- Modern DataThe aim of this module is to provide an introduction to data management and exploration. An overview of current industry standard processes to modern data analysis will be presented, and you will learn to design and plan a predictive analytics project. Basic concepts of data management and retrieval will be discussed. Well established strategies and approaches to data understanding, data preparation and cleaning will be presented.
This course can be studied undefined undefined, starting in undefined.
Please note that all modules are subject to change.
Read more about the structure of postgraduate degrees at Brunel
Careers and your future
Your MSc in Data Science and Analytics from Brunel will equip you to work in leading data science organisations.
Companies seeking to employ our data science graduates include Accenture, AstraZeneca, AXA Insurance, British Airways, Capgemini, Experian, FICO, GE Healthcare, HSBC, nPower, Orange, PayPal, Sopra and Waitrose.
UK entry requirements
- A 2:2 or above UK Honours degree or equivalent internationally recognised qualification from a scientific, engineering, or a numerate subject.
We welcome applicants with other qualifications and industrial experience (relevant to the subject area), who will be assessed on an individual basis. Such applicants may be required to attend an interview and/or aptitude tests
EU and International entry requirements
If you require a Tier 4 visa to study in the UK, you must prove knowledge of the English language so that we can issue you a Certificate of Acceptance for Study (CAS). To do this, you will need an IELTS for UKVI or Trinity SELT test pass gained from a test centre approved by UK Visas and Immigration (UKVI) and on the Secure English Language Testing (SELT) list. This must have been taken and passed within two years from the date the CAS is made.
English language requirements
- IELTS: 6.5 (min 6 in all areas)
- Pearson: 59 (59 in all subscores)
- BrunELT: 63% (min 58% in all areas)
- TOEFL: 90 (min 20 in all)
You can find out more about the qualifications we accept on our English Language Requirements page.
Should you wish to take a pre-sessional English course to improve your English prior to starting your degree course, you must sit the test at an approved SELT provider for the same reason. We offer our own BrunELT English test and have pre-sessional English language courses for students who do not meet requirements or who wish to improve their English. You can find out more information on English courses and test options through our Brunel Language Centre.
Please check our Admissions pages for more information on other factors we use to assess applicants. This information is for guidance only and each application is assessed on a case-by-case basis. Entry requirements are subject to review, and may change.
Fees and funding
2025/26 entry
UK
£14,435 full-time
£7,215 part-time
International
£26,250 full-time
£13,125 part-time
Staged Masters UK/EU: £4,810
Staged Masters International: £8,750
More information on any additional course-related costs.
Fees quoted are per year and are subject to an annual increase.
See our fees and funding page for full details of postgraduate scholarships available to Brunel applicants.
Scholarships and bursaries
Teaching and learning
Our teaching sessions, including lectures, labs, seminars, and tutorials, will primarily be delivered in-person, on campus. There may be instances where other forms of delivery (e.g., online) are adopted, where necessary and/or if appropriate, to enhance the teaching experience. However, you should expect to attend all teaching sessions, examinations and other relevant assessments in-person, on campus.
As our programmes are highly practical and involve group work, we believe that attending teaching sessions on campus provides the best experience, both socially and academically, for you.
Access to a laptop or desktop PC is required for joining online activities, completing coursework and digital exams, and a minimum specification can be found here.
We have computers available across campus for your use and laptop loan schemes to support you through your studies. You can find out more here.
Our Data Science and Analytics master's programme aims to equip you with the qualities and transferable skills necessary for employment. The course is developed with industry in mind and has one or more industrial advisers who are involved in course development and delivery.
Modules are typically taught via lectures and seminars with some lab work. Where appropriate other teaching methods will also be incorporated. All learning is supported by the market leader in Virtual Learning Environments (VLE), the Blackboard Learn system.
Assessments on this programme will include hands-on use of data science software tools as well as report writing. The final MSc Dissertation project will provide you with the opportunity to tackle a challenging problem showcasing the skills acquired. The programme specification includes the list of study and assessment blocks on the programme.
Should you need any non-academic support during your time at Brunel, the Student Support and Welfare Team are here to help.
Assessment and feedback
Your progress will be assessed by a balance of coursework, class tests and exams. Assessments range from written reports/essays through to conceptual/statistical modelling and programming exercises.
Read our guide on how to avoid plagiarism in your assessments at Brunel.