Taught course

Big Data Science (With Industrial Experience)

Institution
Queen Mary, University of London · Electronic Engineering
Qualifications
MSc

Entry requirements

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).

Months of entry

September

Course content

This programme is designed for those who want to pursue a career as data scientists, deriving valuable insights and business relevant information from large amounts of data. You will cover the fundamental statistical (eg machine learning) and technological tools (eg cloud platforms, Hadoop) for large-scale data analysis.

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.

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.

Students will be offered 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. Students will also undertake a large project where they 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.

Industrial Experience

The industrial placement currently takes place towards the end of the first year for a maximum of 12 months. It is the student’s responsibility to secure their placement, the school will offer guidance and support in finding and securing the placement but the onus is on the student to secure the job and arrange the details of the placement.

Currently if you are not able to secure a placement by the end of your second semester we will transfer you onto the 1 year FT taught programme without the Industrial Experience, this change would also be applied to any visa if you were here on a student visa.

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 between the taught component and the project. This 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.

If you do not secure a placement you will be transferred onto the 1 year FT programme.

Why study your MSc in Big data at Queen Mary?

  • Queen Mary has a prestigious history in computing and electronic engineering, we had one of the first Computer Science Departments in the country, and The School of Electronic Engineering and Computer Science is rated in the top 20 universities in the UK for studying computer science and electronic engineering.
  • Nine members of staff hold prestigious awards for research from bodies including the Engineering and Physical Sciences Research Council, Royal Academy of Engineering, Royal Society and the European Research Council.
  • This programme is reviewed and approved by a panel of industrial experts to ensure that it is up-to-date and relevant to the computing sector.

Facilities

The School of Electronic Engineering and Computer Science offers taught postgraduate students their own computing laboratory. MSc students have exclusive use of the top floor in our purpose-built, climate controlled, award winning informatics teaching laboratory (ITL) outside of scheduled laboratory sessions. The ITL hosts over 250 state-of-the-art PCs capable of multimedia production and several laser printers. In addition, there are video conference facilities, seminar rooms, and on-site teaching services and technical support. There are also a number of breakout spaces available to students with full wi-fi access allowing you use your own mobile devices.

The ITL is primarily used for taught laboratory sessions and regularly hosts research workshops and drop-in lab facilities. For postgraduate students on taught and research degrees there are specialist laboratories to use for carrying out research. Our augmented human interaction (AHI) laboratory combines pioneering technologies including full-body and multi-person motion capture, virtual and augmented reality systems and advanced aural and visual display technologies. We also have specialist laboratories in multimedia; telecommunication networks; and microwave antennas. In addition to these spaces, PhD students have generous study space in our research laboratories.

In 2011 we completed the £2m development of new experimental facilities in Antennas and Media and Arts Technology. We formed the Interdisciplinary Informatics Hub in Collaboration with the Schools of Biological and Chemical Sciences and Mathematical Sciences. These laboratories provided a meeting place for postgraduates from the three Schools to interact and exchange ideas.

Information for international students

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.

Please consult our webpage for further information.

Fees and funding

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, here: http://www.qmul.ac.uk/postgraduate/taught/funding_masters/index.html

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 [PDF] for detailed information about postgraduate funding options for Home/EU students.

Read more about alternative sources of funding for Home/EU students and for Overseas students.

Tel: +44 (0)20 7882 5079
email bursaries@qmul.ac.uk

Qualification and course duration

MSc

full time
24 months

Course contact details

Name
EECS General Enquiries
Email
eecs-msc-enquiries@qmul.ac.uk
Phone
+44 (0)20 7882 7333