Data Science and Analytics MSc
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
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.
The Data Science and Analytics MSc programme provides these skills, combining a strong academic programme with hands-on experience of leading commercial technology – and the chance to gain industry certification.
You will develop both your critical awareness of the state-of-the-art in data science and the practical skills that help you apply data science more effectively in the business, science and social world.
The programme is run in conjunction with SAS, a market leader in business analytics software and services, and the largest independent vendor in the business intelligence market.
Brunel's programme is unique in being the only current MSc programme that is fully integrated with SAS, providing the SAS base certification.
The Harvard Business Review calls data science the “sexiest job of the 21st century” – with demand for graduates with SAS skills rapidly rising across financial, retail and government sectors. Data science is now in vogue.
From government, social networks and ecommerce sites to sensors, smart meters and mobile networks, data is being collected at an unprecedented speed and scale – creating an expanding job market for qualified data analysts.
The practical aspects of many of the modules will allow you to gain hands-on experience of several commercial SAS tools (e.g. SAS BASE, Enterprise Guide, Enterprise Miner and Visual Analytics). This experience is designed, in part, to develop skills in preparation for the SAS certification part of the programme.
By the end of the course you should be able to:
- Comprehend the key concepts and nuances of the disciplines that need to be synthesised for effective data science.
- Demonstrate a 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 (i.e. data science).
- Develop a practical understanding of the skills, tools and techniques necessary for the effective application of data science.
- Apply a practical understanding of data science to problems in social, business and scientific domains.
- Evaluate the effectiveness of applied data science in relation to the issues addressed.
Your studies on the course will cover the modules listed below. The practical aspects of many of the modules will allow you to gain hands-on experience of several commercial SAS tools (e.g. SAS BASE, Enterprise Guide, Enterprise Miner and Visual Analytics). That experience is designed, in part, to develop skills for the SAS certification that partners the programme.
The 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). Example topics of study include: understanding and justifying change, change management, digital business models, managing technology risks, ethical issues in change.
Quantitative Data Analysis
The aim of the module is to develop knowledge and skills of the quantitative data analysis methods that underpin data science. You will develop a practical understanding of compulsory methods in data science application and research (e.g. bi-variate and multi-variate methods, regression etc). You will also learn to evaluate the strengths and weaknesses of methods alongside an understanding of how and when to use or combine methods.
High Performance Computational Infrastructures
The aim of the module is to develop knowledge and skills necessary for working effectively with the large-scale data storage and processing infrastructures that underpin data science. Again, you will develop both practical skills and an ability to reflect critically on concepts, theory and appropriate use of infrastructure. Content here covers, highly-scalable data-storage paradigms (e.g. NoSQL data stores) alongside cloud computing tools (e.g. Amazon EC2) and in-memory approaches.
Systems Project Management
This module examines the challenges in information systems project management. Example topics of study include traditional project management techniques and approaches, the relationship between projects and business strategy, the role and assumptions underpinning traditional approaches and the ways in which the state-of-the-art can be improved.
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).
This module will introduce methods of data collection and analysis when conducting empirical research. This research can take place in an organisational setting. Both in the private or the public sector. This module is essential preparation for the dissertation.
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 and augmented reality. An emphasis is also placed on visual storytelling and narrative development.
Learning Development Project
The aim of the module is to develop a team-based integrative solution to a problem/challenge drawn from the business, scientific and/or social domain (as appropriate). Working as part of a small team you will: Refine a coherent set of stakeholder requirements from an open-ended (business, scientific or social) problem/challenge; develop a solution addressing those requirements that coherently draws upon the knowledge and skills of other modules within the programme; effectively evaluate the solution (with stakeholders where appropriate).
Your dissertation is an opportunity to showcase your project management and subject specific skills to potential employers, and also serves as valuable experience and a solid building block if you wish to pursue a PhD on completion of the MSc. You will be encouraged to critically examine the academic and industrial contexts of your research, identify problems and think originally when proposing potential solutions that serve to demonstrate and reflect your ideas.
As preparation for the dissertation, you will be given a grounding in both quantitative and qualitative methods of data collection and analysis appropriate to conducting empirical and/or experimental research.
Read more about the structure of postgraduate degrees at Brunel
and what you will learn on the course.
From an industry perspective, there is an increasingly well-documented skills shortage developing in-and-around data science – as highlighted in the following reports and studies:
- “A shortage of skilled workers in the overall data analytics market is cited as one of the key barriers to further data analytics activity for businesses both globally and in the UK” (source: ‘Seizing the Data Opportunity: A Strategy for UK Data Capability' - 2013 UK government report.
- 46% of respondents in a global survey quoted staff shortages as the most common barrier to implementing data analytics (source: ‘Seizing the Data Opportunity: A Strategy for UK Data Capability', 2013 UK government report).
- Demand for big data staff is predicted to increase by between 13% and 23% per annum, between now and 2017 (source: E-skills UK).
- ‘The data scientist: the sexiest job of the 21st century’ (source: Harvard Business Review article, 2012).
- Businesses that outperform competitors are five times more likely to use analytics strategically than low performers (source: ‘Getting Serious About Analytics: Better Insights, Better Decisions, Better Outcomes’ - Accenture, April 2011).
- Organisations that invest in analytics could help generate £216 billion for the UK economy and create 58,000 new jobs over the next five years (source: Centre for Economics and Business Research report, ‘Data equity: Unlocking the value of big data’, April 2012).
Our Master's programmes aim to equip you with the qualities and transferable skills necessary for employment. Each course is developed with industry in mind and has one or more industrial advisers who are involved in course development and delivery.
The ability to generate effective insight and value from data is increasingly important across all industrial sectors. Data science is thus becoming a feature in a very wide range of industries, including automotive, banking and financial services, energy (e.g. oil and gas), health, management consulting, media and new media, retail and transport.
Given the range of vertical sectors that data science is important to, there are a vast number of companies seeking to employ graduates in this area. These include such organisations as Accenture, AstraZeneca, AXA Insurance, British Airways, Capgemini, Experian, FICO, GE Healthcare, HSBC, nPower, Orange, PayPal, Sopra and Waitrose.
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.
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 2017/18
A UK first or second class Honours degree or equivalent internationally recognised and usually come from a scientific/engineering background and/or a numerate subject area.
Applicants with other qualifications with industrial experience (that is relevant to the subject area) may be considered and will assessed on an individual basis and industrial certifications may be taken into account (such as those provided by organisations such as Microsoft and Sun for example). Such applicants may be required to attend an interview.
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
Module are typically presented in a mixture of lecture and seminar/lab format. However, where appropriate other teaching methods will also be incorporated. All our learning environments are supported by the market leader in Virtual Learning Environments (VLE), the BlackboardLearn system.
Your learning will be evaluated through a combination of in module assessments and more traditional exams, with module specific assessments – for example, presentations within the Learning Development Project.
As an integral part of the programme, you will gain hands-on experience of commercial SAS tools – SAS being the market leader in business analytics software and services, and the largest independent vendor in the business intelligence market.
You will have the opportunity to obtain SAS certification (e.g. SAS Base Programming) which is a recognised industry qualification, following a two week SAS certification ‘boot camp’ preparation course.
Women in Engineering and Computing Programme
Brunel’s Women in Engineering and Computing mentoring scheme provides our female students with invaluable help and support from their industry mentors.
Fees for 2017/18 entry
£9,500 full-time; £4,750 part-time
£17,500 full-time; £8,750 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).