Overview
Statistics is the study of the collection, analysis, interpretation, presentation and organisation of data. Statistical analysis and data analytics is listed as one of the highly desirable skills employers are looking for, and with data becoming an ever increasing part of modern life, the talent to extract information and value from complex data is scarce.
The new Statistics and Data Analytics MSc is designed to train the next generation of statisticians with a focus on the field of data analytics. Employers expect skills in both statistics and computing. This master’s programme will provide a unique and coherent blend of modern statistical methods together with the associated computational skills that are essential for handling large quantities of unstructured data. This programme offers training in modern statistical methodology, computational statistics and data analysis from a wide variety of fields, including financial and health sectors.
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Course content
The programme consists of eight compulsory modules and a statistics with data analytics dissertation. The first term provides a thorough grounding in core programming, statistical and data analysis skills. In addition to acquiring relevant statistical and computational methods, you’ll be encouraged to engage with real commercial and/or industrial problems through a series of inspiring case studies delivered by guest speakers.
On completing this course you’ll be equipped with a range of advanced statistical methods and the associated computational skills for handing large quantities of unstructured data. You’ll develop a critical awareness of the underlying needs of industry and commerce through case studies. You’ll be able to analyse real-world data and to communicate the output of sophisticated statistical models in order to inform decision making processes. You’ll have the necessary computational skills to build and analyse simple/appropriate solutions using statistical Big Data technologies.
Compulsory
- Probability and StochasticsThis module aims to equip students with the basic measure-theoretic and probabilistic concepts and techniques needed for them to be able to apply the modern mathematical theory of finance, and to enable students to use methods of stochastic calculus based on Brownian motion in such a way that they are able to carry out the necessary mathematical manipulations and calculations required for use and critical assessment of the various financial models introduced in other modules of the programme.
- Research Methods and Case StudiesThe aims of this module are to develop students’ knowledge and critical awareness of a variety of research methods, to encourage students to develop critical thinking skills and transferable skills appropriate to their discipline, to enable students to develop an understanding of the current needs of industry and commerce, and to prepare students for their dissertation.
- Computer Intensive Statistical MethodsThis module aims to introduce the students to a range of computational intensive statistical methods, to further develop their skills in correct interpretation and clear reporting of results, and to enable the students to create algorithms for regression models (parametric regression and nonparametric regression) to cope with massive data.
- 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.
- Big Data AnalyticsThe aim of this module is to develop the reflective and practical understanding necessary to extract value and insight from large heterogeneous data sets. Focus is placed on the analytic methods/techniques/algorithms for generating value and insight from the (real-time) processing of heterogeneous data. Content will cover approaches to data mining alongside machine learning techniques, such as clustering, regression, support vector machines, boosting, decision trees and neural networks.
- DissertationThe dissertation aims to enable students to develop a robust understanding of more advanced and practical issues in statistics and data analytics of their choosing, and to demonstrate a students’ ability to engage critically and analytically in literature from the field, building upon relevant concepts and theory covered in the taught element of the degree.
- Financial MarketsThis module aims to equip students with the basic concepts of financial markets, including market terminology and conventions, required for other modules of the programme, and to enable candidates to perform calculations to obtain solutions to basic portfolio optimisation problems using a range of models and techniques.
- Time Series ModellingThis module aims to equip students with the ability to employ different methods for modelling and forecasting time series data, in particular in the context of financial data and forecasting financial risk, and to enable students to apply a range of models and tools to make financial decisions such as risk assessment.
Optional
- Financial and Economic Data Analytics
This course is designed to provide students with fundamental knowledge and skills of how to handle and analyse big data with a focus on financial applications. Students will also gain knowledge on designing and applying machine learning predictive models in finance.
- Fundamentals of Machine LearningThis module aims to equip students with the knowledge and ability to use modern regression and classification methods with different types of data, to enable students to apply a range of models and tools to variable selection and model selection.
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Please note that all modules are subject to change.
Read more about the structure of postgraduate degrees at Brunel
Careers and your future
By the end of this programme you’ll have acquired an advanced level of statistical knowledge and data analytical skills. This will allow you to work as an independent expert within a multidisciplinary team that designs, performs, analyses and reports about applied scientific research.
You’ll be equipped to pursue a career in data science, the financial sector or the health sector. Areas you might be interested in could include big processing companies (Accenture, Oracle Corporation), the financial sector (JP Morgan), pharmaceuticals (GSK), government agencies (the Office of National Statistics) and data science departments within universities.
UK entry requirements
A 2.2 (or above) UK honours degree or equivalent, internationally recognised qualification in numerate disciplines with adequate content in statistics, including: Mathematics, Statistics, Computational Statistics, Biostastics, Medical Statistics, Social Statistics, Machine Learning, Stochastic models, Probability Statistics, Statistical Modelling, Applied Statistics or Econometrics, Economics, Actuarial Science, Mathematics for Data Science, Applied Mathematics.
Other academic profiles and students with relevant work experience will be considered on a case by case basis.'
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 (min 5.5 in all areas)
- Pearson: 59 (59 in all sub scores)
- BrunELT: 58% (min 55% in all areas)
- TOEFL: 77 (min R18, L17, S20, W17)
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
2024/25 entry
UK
£13,750 full-time
£6,875 part-time
International
£25,000 full-time
£12,500 part-time
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
Lectures will primarily be delivered in-person on-campus, though some may be delivered online either as pre-recorded or live sessions. The expectation is that students will attend all timetabled on-campus lectures, and that online lectures will be viewed by students in advance of related on-campus activities.
Tutorials & discussion-based sessions will primarily be delivered in-person on campus, though some may be delivered online in order to supplement on-campus learning. The expectation is that students will attend all timetabled on-campus or online tutorials.
Computing Labs will primarily be delivered in-person on campus, though some may be delivered online in order to supplement on-campus learning. The expectation is that students will attend all timetabled on-campus or online computing labs. Students will be provided with access to the specialised software required.
Support/resources Learning materials for every module will be made available online, through the University’s Virtual Learning Environment, Brightspace.
Assessments will be varied, and may include: CAA (computer aided assessment) tests, written coursework assessments (including software tasks), presentations (in-person or video presentations) and written examinations. Students will be expected to attend assessments in-person on campus.
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.
Mathematics at Brunel has an active and dynamic research centre and many of our lecturers are widely published and highly recognised in their fields. Their work is frequently supported by external grants and contracts with leading industry and government establishments. Lecturers are consequently at the frontier of the subject and in active contact with modern users of mathematics. This means that you can be assured that our academics are teaching you truly up-to-date methods and you’ll benefit from a wide range of expertise across the different areas of mathematics.
You’ll be taught using a range of teaching methods, including lectures, computer labs and discussion groups. Lectures are supplemented by computer labs and seminars/exercise classes and small group discussions. The seminars will be useful for you to carry out numerical data analysis, raise questions arising from the lectures, exercise sheets, or self-studies in an interactive environment.
Support for academic and personal growth is provided through a range of workshops covering topics such as data protection, critical thinking, presentation skills and technical writing skills.
You’ll also complete an individual student project supervised by a relevant academic on your chosen topic.
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
The assessment of all learning outcomes is achieved by a balance of coursework and examinations. Assessments range from written reports/essays, group work, presentations through to conceptual/statistical modelling and programming exercises, according to the demands of particular modular blocks. Additionally, class tests are used to assess a range of knowledge, including a range of specific technical subjects.
Read our guide on how to avoid plagiarism in your assessments at Brunel.