Find out more about studying Data Science and Analytics MSc by Research at Brunel University of London? We've gathered all the key details, entry requirements, modules, fees, and more. Take the next step by booking an open day to explore it for yourself.
MRes - Master of Research
Brunel University of London
Full Time
Sep 2026
1 Year
The MSc by Research comprises two terms of taught modules – please see below for details – amounting 60 credits. These are followed by a third term of research methods in information systems and an extended dissertation which together make up the remaining 120 credits.You will have the opportunity to work with Brunel doctoral students and some of the world’s leading researchers within a range of research groups, including the Intelligent Data Analysis Group, Modelling & Simulation Group and Human Computer Interaction group, all based within our Department of Computer Science.Alongside the technical content of the programme, you will develop a broader set of skills including study skills and employment skills through teamwork, guest lectures or workshops with industry, and dissertation projects with industrial/academic collaborations.The programme offers both exciting career prospects in industry and research, or a route to further studies in a PhD pathway.Your MSc by Research in Data Science and Analytics from Brunel focuses on creating T-shape researchers. It will equip you with critical, research-led awareness of the state-of-the-art in data science together with practical skills necessary to create value in application to business, scientific and/or social domains. Your MSc by Research in Data Science and Analytics from Brunel will equip you to work in leading data science organisations and/or pursuit further research qualifications at a PhD level.
The 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 statistical 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.
The 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 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.
This 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.
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