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Statistics with Data Analytics MSc

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Mode of study

1-year full-time; 2 year part-time

UCAS Code

STATDATAFTD

Start date

September

Statistics with Data Analytics MSc

Overview

Please note the application deadline for this course is Friday 25th August 2017. Any applications after this date will be considered on an individual basis, subject to course vacancies.

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.

Aims

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.

Enquiries

The Postgraduate Admissions Secretary

Department of Mathematics

Email mscmaths@brunel.ac.uk


Course Content

Programme structure

Your studies on the course will cover the modules listed below.

Compulsory modules

  • Quantitative Data Analysis
  • Research Methods and Case Studies
  • Computer Intensive Statistical Methods
  • Modern Regression and Classification
  • Data Visualisation
  • Big Data Analytics
  • Time Series Modelling
  • Network Models
  • Dissertation

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

Special Features

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. 

Teaching and Assessment

Teaching

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.

Assessment

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.

 

Employability

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

Fees for 2017/18 entry

UK/EU students: £9,500 full-time; £4,750 part-time

International students: £17,500 full-time; £8,750 part-time

Additional course related costs

Entry Criteria 2017/18

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.