Taught course

Big Data Science

Queen Mary, University of London · Electronic Engineering

Entry requirements

You should have a good second-class degree or above (good 2:1 minimum for Industrial Experience option) 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 whose first language is not English, we require English language qualifications IELTS 6.5 or TOEFL 92 (internet based).

Months of entry


Course content

We offer Industrial Experience options on all our full-time taught MSc programmes, which combine academic study with a one-year industrial placement between your taught modules and summer project. Taking the Industrial Experience option as part of your degree gives you a route to develop real-world, practical problem-solving skills gained through your programme of study in a professional context.

This can give you an important edge in the graduate job market. As a leading research School, we have excellent links with industry. We also employ dedicated staff to help you arrange your year in industry. The Industrial Experience programmes are highly competitive and attract the best students given the limited availability of placements. We are unable to guarantee all students secure an industrial placement, as our industrial partners conduct their own employment application and interview processes.

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 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.

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.

Fees and funding

UK students
International students

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.

Qualification and course duration


full time
12 months
part time
24 months

Course contact details

School of Electronic Engineering and Computer Science
+44 (0)20 7882 7333