Statistics with Data Analytics MSc
Applications are welcome from candidates seeking admission for the programme beginning in September 2018.
About the course
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.
Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. The aim of the MSc Statistics and Data Analytics is to produce graduates that:
- are equipped with a range of advanced statistical methods and the associated computational skills for handling large quantities of unstructured data;
- have developed a critical awareness of the underlying needs of industry and commerce through relevant case studies;
- are able to analyse real-world data and to communicate the output of sophisticated statistical models in order to inform decision making processes;
- have the necessary computational skills to build and analyse simple/appropriate solutions using statistical Big Data technologies.
Contact our Enquiries team.
Course Enquiries: +44 (0)1895 265599 (before you submit an application)
Admissions Office: +44 (0)1895 265265 (after you submit an application)
Your studies on the course will cover the modules listed below.
Quantitative Data Analysis
The aim of the 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 (e.g., 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.
Research Methods and Case Studies
The aims of this module are to develop students’ knowledge and critical awareness of a variety of research methods; encourage students to develop critical thinking skills and transferable skills appropriate to their discipline, enable students to develop an understanding of the current needs of industry and commerce, prepare students for their dissertation.
Computer Intensive Statistical Methods
In this course, you will learn six computer intensive methods by R. These six methods are:
- Basic Monte-Carlo simulation methods
- Simulating from specified distributions which include inverse method and rejection sampling;
- Aggregation and compression technique based on the “divide-and-conquer” strategy to cope with massive data in regression analysis;
- Bootstrap which is used in several contexts, most commonly to provide a measure of accuracy of a parameter estimate or of a given statistical learning method;
- Randomization testing and Monte Carlo approach such as t-test without the normal assumption;
- Monte-Carlo integration such as importance sampling for numerical integration with good accuracy.
Modern Regression and Classification
- Demonstrate a working knowledge of the theory of Lasso and its different versions.
- Develop a working knowledge of the research concepts involved in p>n.
- Select a classification for a specific application and interpret the output of the statistical analysis.
- Fit Lasso-based models to data from a variety of applied domains.
- Implement variable selection and statistical classification, using a software package.
- Interpret and visualise the output of the statistical inference.
The aim of the module is to develop the reflective and practical understanding necessary to visually present insight drawn from large heterogeneous data sets (e.g., to 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 visualization process will be explored and an emphasis placed on visual storytelling and narrative development.
Big Data Analytics
The aim of the 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 (e.g., clustering, regression, support vector machines, boosting, decision trees and neural networks).
Time Series Modelling
This course 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 apply a range of models and tools to make financial decisions such as risk assessment.
This courses aims to equip students with the ability to employ different methods for modelling networks, and to apply a range of network models and tools to a number of applied domains. The contenst include
- Demonstrate a working knowledge of the theory of network models and of the research concepts involved in inferring and summarising networks.
- Select a network model for a specific application.
- Fit network models to data from a variety of applied domains.
- Implement statistical network models, using a software package.
- Interpret and visualise the output of the statistical inference.
Statistics with Data Analytics Dissertation
Towards the end of the Spring Term, students will choose a topic for an individual research project, which will lead to the preparation and submission of an MSc dissertation. The project supervisor will usually be a member of the Brunel Statistics or Financial Mathematics group. In some cases the project may be overseen by an external supervisor based in industry or another academic institution.
Read more about the structure of postgraduate degrees at Brunel and what you will learn on the course.
Read more about the structure of postgraduate degrees at Brunel
and what you will learn on the course.
Students on this master’s programme will have acquired an advanced level of statistical knowledge and data analytical skills. This will allow them to work as an independent expert within a multidisciplinary team that designs, performs, analyses and reports about applied scientific research.
Graduates will be able to find employment in the data science/financial/health sectors, like big data 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.
At Brunel we provide many opportunities and experiences within your degree programme and beyond – work-based learning, professional support services, volunteering, mentoring, sports, arts, clubs, societies, and much, much more – and we encourage you to make the most of them, so that you can make the most of yourself.
» More about Employability
Entry Criteria 2018/19
A UK honours degree or equivalent, internationally recognised qualification in mathematics/statistics or other numerate disciplines with adequate content in statistics.
Entry criteria are subject to review and change each academic year.
International and EU Entry Requirements
If your country or institution is not listed or if you are not sure whether your institution is eligible, please contact Admissions
This information is for guidance only by Brunel University London and by meeting the academic requirements does not guarantee entry for our courses as applications are assessed on case-by-case basis.
English Language Requirements
- IELTS: 6.5 (min 6 in all areas)
- Pearson: 58 (51 in all subscores)
- BrunELT: 65% (min 60% in all areas)
Brunel University London strongly recommends that if you will require a Tier 4 visa, you sit your IELTS test at a test centre that has been approved by UK Visas and Immigration (UKVI) as being a provider of a Secure English Language Test (SELT). Not all test centres have this status. The University can accept IELTS (with the required scores) taken at any official test centre or other English Language qualifications we accept as meeting our main award entry requirements.
However, if you wish to undertake a Pre-sessional English course to further improve your English prior to the start of your degree course, you must sit the test at an approved SELT provider. This is because you will only be able to apply for a Tier 4 student visa to undertake a Pre-sessional English course if you hold a SELT from a UKVI approved test centre. Find out more information about it.
Brunel also offers our own BrunELT English Test and accepts a range of other language courses. We also have Pre-sessional English language courses for students who do not meet these requirements, or who wish to improve their English. Find out more information about English course and test options.
Teaching and Assessment
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.
The first term provides a thorough grounding in core programming, statistical and data analysis skills. In addition to acquiring relevant statistical and computational methods, students are encouraged to engage with real commercial and/or industrial problems through a series of inspiring case studies delivered by guest speakers. 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.
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.
The Statistics Group is a growing, highly-research active group, with collaborations across industry and academia, including engineering and pharmaceutical companies, Cambridge University and Imperial College London
Brunel’s Mathematics department is a member of the London Graduate School in Mathematical Finance. This consortium of mathematical finance groups comprises Birkbeck College, Brunel University London, Imperial College London, King’s College London, London School of Economics and Political Science and University College London.
Fees for 2018/19 entry
£9,750 full-time; £4,875 part-time
£18,000 full-time; £9,000 part-time
Additional course related costs
Read about funding opportunities available to postgraduate students
UK/EU students can opt to pay in six equal monthly instalments: the first instalment is payable on enrolment and the remaining five by Direct Debit or credit/debit card.
Overseas students can opt to pay in two instalments: 60% on enrolment, and 40% in January for students who commence their course in September (or the remaining 40% in March for selected courses that start in January).