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Data Science MSc

Part of: Computer and Data Science

This programme is designed to address the growing demand for highly skilled data scientists and engineers capable of combining advanced computer science techniques with modern statistical methods to extract insights and create data-driven services. We offer world-leading expertise and industry partnerships to equip students with essential skills in statistical data modeling, visualisation, machine learning, and domain-specific applications like computer vision and natural language processing.

  • Create new business services that are based on insights learnt from data.
  • Learn to create automated prediction, recommendation and classification systems.
  • Gain advanced mathematical and technical skills including machine learning and deep learning.
  • Benefit from our world-leading research as well as our strategic partnerships with leading technological companies.

Study options

Starting in
September 2025
Location
Mile End
Fees
Home: £12,850
Overseas: £33,500
EU/EEA/Swiss students
Apply nowRegister your interest

What you'll study

You will cover fundamental statistical and analytical concepts (such as machine learning) and technological tools (such as cloud platforms, Spark) for large-scale data analysis. Through your taught modules, you will examine:

  • Statistical data modelling, data visualisation and prediction.
  • Machine learning techniques for cluster detection, and automated classification.
  • Techniques for processing massive amounts of data.
  • Domain-specific techniques for applying data science, including: computer vision, social media analysis, intelligent sensing and internet of things.
  • Case study-based projects that show the practical application of key skills in real industrial and research scenarios.

You will also undertake a large project where you will demonstrate the application of data science skills in a complex scenario.

This degree is accredited by BCS, The Chartered Institute for IT, for the purposes of partially meeting the academic requirement for registration as a Chartered IT Professional. This degree is also accredited by BCS on behalf of the Engineering Council, for the purposes of partially meeting the academic requirement for registration as a Chartered Engineer.

Structure

  • Five compulsory modules
  • Three elective modules
  • Research project 

All students studying MSc Data Science will undertake five compulsory modules covering key concepts, including probability and statistics, machine and deep learning, and big data processing. Elective modules are divided between two streams, which students will choose from.

These streams will allow you to further develop your professional profile and graduate with industry-relevant expertise. 

Find out more about each module below, by looking them up in the module directory.

Stream 1: Data Science stream

This stream focuses on building scientific and analytics solutions to extract value and knowledge from data of different modalities, encountered in many business and research activities.

Semester A

  • Applied Statistics
  • Data Mining
  • Principles of Machine Learning
  • Natural Language Processing

Semester B

  • Big Data Processing
  • Neural Networks and Deep Learning
  • Digital Media and Social Networks
  • Risk and Decision Making for Data Science and AI

Semester C:

  • Project

Stream 2: Data Engineering stream

This stream focuses on building scalable environments to ingest, process, manage and serve massive amounts of data, to create data-driven products and services.

Semester A

  • Applied Statistics
  • Data Mining
  • Principles of Machine Learning
  • Cloud Computing

Semester B

  • Big Data Processing
  • Neural Networks and Deep Learning
  • Data Semantics
  • Distributed Systems

Semester C:

  • Project
Master Journey Webinars

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Assessment

  • 67% Modules
  • 33% Research project
  • Modules are assessed through a combination of coursework and written examinations.
  • Your research project will be evaluated by thesis, presentation and viva examination.
Harriet David, MSc Data Science (Formally known as MSc Big Data Science), 2022

"I chose to study at Queen Mary because of the exciting research happening at the university, including in area of data science. I chose this course because of the opportunity to be taught by experts in the field. I have enjoyed completing coursework for my modules, since this has allowed me to gain significant practical Python experience. My favourite module has been Neural Networks and Deep Learning. I have enjoyed learning about the different types of neural networks and also building neural network architectures using PyTorch."

 

—

Harriet David, MSc Data Science (Formally known as MSc Big Data Science), 2022

Teaching

Teaching for all modules includes a combination of lectures, seminars and use of a virtual learning environment. Each module provides contact time with your lecturers, supported by lab work and directed further study.

You will be assigned an Academic Advisor who will guide you in both academic and pastoral matters throughout your time at Queen Mary.

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.

Where you'll learn

Facilities

The School has excellent bespoke facilities, including:

  • Augmented human interaction (AHI) laboratory
  • Dedicated computing cluster for Big Data processing jobs
  • Informatics teaching laboratory with 350 state-of-the-art computers, and GPUs for machine learning 
  • Antenna measurement laboratory
  • Listening room
  • Media and arts technology studios
  • Performance lab
  • Robotics laboratory (ARQspace)

Campus

Teaching is based at Queen Mary’s main Mile End campus, one of the largest self-contained residential campuses in the capital. Our location in the heart of London’s East End offers a rich cultural environment.

We have invested £105m in new facilities over the past five years to offer our students an exceptional learning environment. Recent developments include the £39m Graduate Centre, providing 7,700 square metres of learning and teaching space.

The campus is 15 minutes from Central London by tube, where you will have access to many of the University of London’s facilities, such as the Senate House library.

About the School

School of Electronic Engineering and Computer Science

The School of Electronic Engineering and Computer Science carries out world-class research – and applies it to real-world problems. Being taught by someone who is changing the world with their ideas makes for exciting lectures, and helps you to stay ahead of the curve in your field. 99 per cent of our research is classed as ‘world-leading’ or ‘internationally excellent’ (REF 2021).

We are proud of our excellent student-staff relations, and our diverse student body, made up of learners from more than 60 countries.

The School has a close-knit student community, who take part in competitions and extracurricular lab activities.

Contact us

School of Electronic Engineering and Computer Science

Career paths

Our postgraduates go on to work in a wide variety of careers and sectors, including technology, healthcare, finance, consulting, marketing and academia. The broad range of skills gained through programmes in this School, coupled with multiple opportunities for extra-curricular activities and work experience, has enabled postgraduates to move into careers such as:

  • Machine Learning Researcher 
  • Data Scientist
  • Head of Data Engineering 
  • Big Data Analyst
  • Business Analyst
  • Technical Analyst

In organisations including:

  • IBM
  • Dataiku
  • Accenture
  • Blackrock
  • Credit Suisse
  • NHS

Fees and funding

Full-time study

September 2025 | 1 year

Conditional deposit

Home: £2000

Overseas: £2000
Information about deposits

Queen Mary alumni can get a £1000, 10% or 20% discount on their fees depending on the programme of study. Find out more about the Alumni Loyalty Award

Funding

There are a number of ways you can fund your postgraduate degree.

Our Advice and Counselling service offers specialist support on financial issues, which you can access as soon as you apply for a place at Queen Mary. Before you apply, you can access our funding guides and advice on managing your money:

Entry requirements

UK

Degree requirements

A 2:1 or above at undergraduate level in Electronic Engineering, Computer Science, Software Engineering, Information Technology or a related discipline.

Other routes

Other education backgrounds can be considered subject to demonstrating satisfactory knowledge of programming. 

Applicants with a good 2:2 degree (55% or above) will be considered on an individual basis.

Find out more about how to apply for our postgraduate taught courses.

International

English language requirements

The English language requirements for our programmes are indicated by English bands, and therefore the specific test and score acceptable is based on the band assigned to the academic department within which your chosen course of study is administered. Note that for some academic departments there are programmes with non-standard English language requirements.

The English Language requirements for entry to postgraduate taught and research programmes in the School of Electronic Engineering and Computer Science falls within the following English band:

Band 4: IELTS (Academic) minimum score 6.5 overall with 6.0 in each of Writing, Listening, Reading and Speaking

We accept a range of English tests and qualifications categorised in our English bands for you to demonstrate your level of English Language proficiency. See all accepted English tests that we deem equivalent to these IELTS scores.

Visas and immigration

Find out how to apply for a student visa.

Contact

School of Electronic Engineering and Computer Science

Postgraduate Admissions

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