Postgraduate Open Afternoon 5 September 2018
2 -5pm, Find out more
Owing to the popularity of this course, we regret we will no longer be accepting applications after 11 July 2018.
The Big Data science movement is transforming how Internet companies and researchers over the world address traditional problems. Big Data refers to the ability of exploiting the massive amounts of unstructured data that is generated continuously by companies, users, devices, and extract key understanding from it. A Data Scientist is a highly skilled professional, who is able to combine state of the art computer science techniques for processing massive amounts of data with modern methods of statistical analysis to extract understanding from massive amounts of data and create new services that are based on mining the knowledge behind the data. The job market is currently in shortage of trained professionals with that set of skills, and the demand is expected to increase significantly over the following years.
If you are looking to pursue a career as a data scientist, this programme is designed for you. You will cover the fundamental statistical (e.g. machine learning) and technological tools (e.g. cloud platforms, Hadoop) for large-scale data analysis.
The course leverages the world-leading expertise in research at Queen Mary with our strategic partnership with IBM and other leading IT sector companies to offer to students a foundational MSc on the field of Data Science. The MSc modules cover the following aspects:
- Statistical Data Modelling, data visualization and prediction
- Machine Learning techniques for cluster detection, and automated classification
- Big Data Processing techniques for processing massive amounts of data
- Domain-specific techniques for applying Data Science to different domains: Computer Vision, Social Network Analysis, Bio Engineering, Intelligent Sensing and Internet of Things
- Use case-based projects that show the practical application of the skills in real industrial and research scenarios.
You will attend lectures that explain the core concepts, techniques and tools required for large-scale data analysis. Laboratory sessions and tutorials will put these elements to practice through the execution of use cases extracted from real domains. You will also undertake a large project where you will demonstrate the application of Data Science skills in a complex scenario.
The programme is offered by academics from the Networks, Centre for Intelligent Sensing, Risk and Information Management, Computer Vision and Cognitive Science research groups from the School of Electronic Engineering and Computer Science. This is a team of more than 100 researchers (academics, post-docs, research fellows and PhD students), performing world leading research in the fields of Intelligent Sensing, Network Analytics, Big Data Processing platforms, Machine Learning for Multimedia Pattern Recognition, Social Network Analysis, and Multimedia Indexing.
The industrial placement takes place from the September following the taught part of the MSc and is for a maximum of 12 months. While it is your responsibility to secure a placement, the EECS Placement Team will provide support by assisting with applications and with interview preparation.
The industrial placement consists of 8-12 months spent working with an appropriate employer in a role that relates directly to your field of study. The placement is currently undertaken after you have completed and passed the taught component of the degree and submitted your MSc project. The placement will provide you with the opportunity to apply the key technical knowledge and skills that you have learnt in your taught modules and will enable you to gain a better understanding of your own abilities, aptitudes, attitudes and employment potential. The module is only open to students enrolled on a programme of study with integrated placement.
In the event that you are unable to secure a placement we will transfer you onto the 1 year FT taught programme without the Industrial Experience. This change will also apply to any student visa you hold at the time.
Why study your MSc in Big data at Queen Mary?
Our research-led approach
Your tuition will be delivered by field leading academics engaged in world class research projects in collaboration with industry, external institutions and research councils.
Our strong links with industry
- We have collaborations, partnerships, industrial placement schemes and public engagement programmes with a variety of organisations, including Vodafone, Google, IBM, BT, NASA, BBC and Microsoft
- Full-time MSc with Industrial Experience option available on our taught MSc programmes. You have the option to complete over two years, with a year of work experience in industry.
- Industrial projects scheme - To support industrial experience development, you can choose to do your final project in collaboration with an industrial partner.
Accredited by BCS, the Chartered Institute for IT for the purposes of fully meeting the academic requirement for registration as a Chartered IT Professional.
MSc Big Data is currently available for one year full-time study, two years part-time study.
The programme is organised in three semesters. The first semester is composed by three core modules plus one optional module that cover the foundational techniques and tools employed for Big Data Science analysis.
The second semester has four modules that are chosen among a set of options. The module selection allows students to focus on domain-specific research or industry applications for Big Data Science. Module options allow students to specialize in several areas: Computer Vision, Internet Services (Semantic Web and Social Media), Business, and Internet of Things.
Students carry out a large project full time in the third semester, after agreeing to a topic and supervisor in the first semester, and completing the preparation phase over the second semester.
Undertaking a masters programme is a serious commitment, with weekly contact hours being in addition to numerous hours of independent learning and research needed to progress at the required level. When coursework or examination deadlines are approaching independent learning hours may need to increase significantly. Please contact the course convenor for precise information on the number of contact hours per week for this programme.
Part-time study options often mean that the number of modules taken is reduced per semester, with the full modules required to complete the programme spread over two academic years. Teaching is generally done during the day and part-time students should contact the course convenor to get an idea of when these teaching hours are likely to take place. Timetables are likely to be finalised in September but you may be able to gain an expectation of what will be required.
The modules listed below provide some general guidance on what you may be expected to learn during each semester and year of this degree. The exact modules available may vary depending on staff availability, research interests, new topics of study, timetabling and student demand.
- Applied Statistics (15 credits)
- Big Data Processing (15 credits)
- Data Mining (15 credits)
Select one option from:
- Machine Learning (15 credits)
- Introduction to IOT (15 credits)
- Semi-Structured Data and Advanced Data Modelling (15 credits
- Introduction to Object Oriented Programming (15 credits)
Four options from:
- The Semantic Web (15 credits)
- Digital Media and Social Networks (15 credits)
- Bayesian Decision and Risk Analysis (15 (credits)
- Cloud Computing (15 credits)
- Data Analytics (15 credits)
- Deep Learning and Computer Vision (15 credits)
- Machine Learning for Visual Data Analytics (15 credits)
- Project (60 credits)
Please note module availability is subject to change.
We aim to deliver your programme so that it closely matches the way in which it has been described to you by QMUL in print, online, and/or in person. Please be assured that we review our modules on a regular basis, in order to continue to offer innovative and exciting programmes.
Please check the School website for further module information.
Jennifer Richards, Postgraduate Administrator
School of Electronic Engineering and Computer Science
Tel: +44 (0)20 7882 7333
You should have a good Honours degree (first or upper-second class honours) in electronic engineering, computer science, mathematics, or a related discipline. Applicants with unrelated degrees will be considered if there is evidence of equivalent industrial experience.
For international students we require English language qualifications IELTS 6.5 or TOEFL 92 (internet based).
Learning and teaching
As a student at Queen Mary, you will play an active part in your acquisition of skills and knowledge. Teaching is by a mixture of formal lectures and small group seminars. The seminars are designed to generate informed discussion around set topics, and may involve student presentations, group exercise and role-play as well as open discussion. We take pride in the close and friendly working relationship we have with our students. You are assigned an Academic Adviser who will guide you in both academic and pastoral matters throughout your time at Queen Mary.
Teaching for all modules includes a combination of lectures, seminars and a virtual learning environment. Each module provides 36 hours of contact time, supported by lab work and directed further study.
For every hour spent in classes you will be expected to complete further hours of independent study. Your individual study time could be spent preparing for, or following up on formal study sessions; reading; producing written work; completing projects; and revising for examinations.
The direction of your individual study will be guided by the formal study sessions you attend, along with your reading lists and assignments. However, we expect you to demonstrate an active role in your own learning by reading widely and expanding your own knowledge, understanding and critical ability.
Independent study will foster in you the ability to identify your own learning needs and determine which areas you need to focus on to become proficient in your subject area. This is an important transferable skill and will help to prepare you for the transition to working life.
Modules are assessed through a combination of coursework and written examinations. You will also be assessed through an individual project.
The MSc research project will be conducted under close supervision throughout the academic year, and is evaluated by thesis, presentation and viva examination.
Tuition fees for Home and EU students2018/19 Academic Year
Thick Sandwich £9,250
Part-time study is not available for this course
Tuition fees for International students2018/19 Academic Year
Thick Sandwich £19,500
Part-time study is not available for this course
There are a number of sources of funding available for Masters students.
These include a significant package of competitive Queen Mary University of London (QMUL) bursaries and scholarships in a range of subject areas, as well as external sources of funding.
Queen Mary bursaries and scholarships
We offer a range of bursaries and scholarships for Masters students including competitive scholarships, bursaries and awards, some of which are for applicants studying specific subjects.
Find out more about QMUL bursaries and scholarships.
Alternative sources of funding
Home/EU students can apply for a range of other funding, such as Professional and Career Development Loans, and Employer Sponsorship, depending on their circumstances and the specific programme of study.
Overseas students may be eligible to apply for a range of external scholarships and we also provide information about relevant funding providers in your home country on our country web pages.
Download our Postgraduate Funding Guide for detailed information about postgraduate funding options for Home/EU students.
Tel: +44 (0)20 7882 5079
Other financial help on offer at Queen Mary
We offer one to one specialist support on all financial and welfare issues through our Advice and Counselling Service, which you can access as soon as you have applied for a place at Queen Mary.
Our Advice and Counselling Service also has lots of Student Advice Guides on all aspects of finance including:
Tel: +44 (0)20 7882 8717