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Data Analytics MSc

Part of: Mathematical Sciences and Computer and Data Science

In today's data-driven economy, organisations need data experts to extract clear insights and inform decision making.

This masters will teach you the core mathematical principles of data analysis and AI, and how to apply these to real-world scenarios. You will also develop the programming, machine learning and visualisation skills employers need.  

Our unique blend of key theoretical knowledge and in-demand practical skills will help you future-proof your career in Data Analytics and AI.

  • Build a solid foundation in mathematics and statistics, setting you up for long-term career success.
  • Develop sought-after programming skills, working with Python, R and C++.
  • Apply what you learn to real-world datasets from a wide variety of sectors.
  • Learn from experienced educators, including industry practitioners and Fellows of the Alan Turing Institute for Data Science and AI.
  • Choose your own specialism to develop the professional skills you need for the job you want. 

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

In the first semester, you will complete the compulsory modules that provide a foundation in data analytics, machine learning, and the statistics of data analysis. You will build upon this knowledge working with industry-standard tools and software.

In the second semester, you will decide on your specialism. This part of the programme has been designed to prepare you for specific career paths, so what you study will depend on what you wish to do in the future.

Our specialisms, also known as streams, can be found below. We've highlighted the skills you will develop and the potential career paths for each stream.

We offer all students the opportunity to take the Microsoft Office Specialist qualification which includes valuable expertise in Excel.

Applied Machine Learning

You will learn to use mathematical, computational and statistical techniques to design, implement, and evaluate machine learning models to solve real-world problems. Potential career opportunities in developing and deploying predictive tools in Finance, Healthcare, Retail, and Manufacturing sectors among others.

Pattern Recognition and Deep Learning

You will learn to use mathematical, computational and statistical techniques extract key insights from different types of data. Potential career opportunities in Tech, Finance, Healthcare, Retail, Automotive, and Manufacturing sectors.

Statistical Inference

You will learn to use mathematical, computational and statistical techniques to analyze data, draw meaningful conclusions, and contribute to evidence-based decision-making across application domains. Potential career opportunities in Finance, Healthcare, Retail, and Manufacturing sectors, among others.

In the third semester, over the summer, you will work alongside one of our researchers on an independent research project. This will consolidate your learning and allow you to develop strong applied data science skills.

You do not need to be a programming expert. You will explore a variety of data analysis tools (such as R and Python), which will allow you to choose which technologies you want to specialise in.

Structure

Structure


In Semester A ,you will take four modules:

  • Machine Learning with Python
  • Storing, Manipulating, and Visualising Data
  • Probability and Statistics for Data Analysis
  • Programming with Python

In Semester B, you can choose one of three streams:

Applied Machine Learning

  • Forecasting with AI
  • Computational Statistics in R

Choose two electives from:

  • Bayesian Statistics
  • SAS for Business Intelligence
  • Optimisation for Business Processes
  • Financial Data Analytics
  • Digital and Real Asset Analytics

Pattern Recognition and Deep Learning

  • Neural Networks and Deep Learning
  • Advanced Machine Learning

Choose two electives from:

  • Graphs and Networks
  • Forecasting with AI
  • Bayesian Statistics
  • Computational Statistics with R

Statistical Inference

  • Bayesian Statistics
  • Computational Statistics with R

Choose two electives from:

  • Graphs and Networks
  • Neural Networks and Deep Learning
  • Forecasting with AI
  • Advanced Machine Learning
Master Journey Webinars

Master Journey Webinars

Your Masters Journey Webinars: Join our engaging webinar series designed to guide you through every step of your masters journey.

Register now

Information about the modules for 2025/26 is available here

Assessment

  • 67% Modules
  • 33% Research project
  • You will be assessed by a mixture of formal examinations and coursework in your taught modules
  • You will undertake more self-directed work in completing your final Data Analytics Project

Research project

Examples of possible projects include: 

  • Time series analysis
  • Exploratory data analysis on a dataset
  • Performance and comparative analysis of state of the art techniques
  • Theoretical models of data
  • Complex systems
  • Dynamical systems
  • Topological data analysis
  • Experimental design with data 
  • Statistical aspects of data analytics techniques

Teaching

You will learn primarily through a combination of lectures and tutorials, in addition to a significant amount of independent study and research.

You will be assigned an Academic Adviser who will guide you throughout your time at Queen Mary. The School of Mathematical Sciences also has a dedicated Student Support Officer to provide you with advice and guidance, with a focus on non-academic issues.

 

Where you'll learn

Facilities

  • Our recently refurbished, £18m Mathematical Sciences building with high-quality teaching rooms, private and group study areas and a new social hub
  • A shared office and dedicated computer lab with Bloomberg terminals for MSc students
  • Library access to 8,000 mathematical books and subscriptions to a large number of mathematical journals
  • On-campus accommodation for all new full-time postgraduate students from outside London

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. As well as the Mathematical Sciences building, this includes the new Graduate Centre, providing 7,700 square metres of learning and teaching space.

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

About the School

School of Mathematical Sciences

Research in the School of Mathematical Sciences covers a diverse range of subjects in pure and applied mathematics, and is consolidated into research groups reflecting the School’s key strengths. 

We have  invested approximately £18 million in our building to provide state-of-the-art research, teaching and study facilities for staff and students.

The University holds a university-level Silver Award for the Athena SWAN Charter, which recognises and celebrates good employment practice for women working in mathematics, science, engineering and technology in Higher Education and research. The School of Mathematical Sciences holds its own department-level Athena Swan Bronze award.  

We are a registered Supporter of the LMS Good Practice Scheme. 

In the most recent Research Excellence Framework (REF 2014), Queen Mary ranked ninth in the UK among multi-faculty universities and fifth for our percentage of 3* and 4* research outputs.

Contact us

School of Mathematical Sciences

Career paths

In our data-driven economy, companies are seeking highly numerate data experts who can use statistical techniques and the latest technologies to extract clear insights to inform every aspect of their strategy and operations.

Students who have graduated from this programme have gone into a variety of data analysis, data science and software development roles including Data Science Consultant, AI Scientist, Pricing Analyst, Customer Insights Analyst, Senior Product Data Analyst, APP Development Specialist, Quantitative Risk Analyst, Market Risk Analyst and more. Recent employers of our graduates include British Airways, Ocado, Accenture, PwC, KPMG, UBS, BlackRock, London Stock Exchange Group, Canary Wharf Group, Tata Consultancy Services and Bloomberg.

 

Fees and funding

Full-time study

September 2025 | 1 year

Conditional deposit

Home: Not applicable

Overseas: £2000
Information about deposits

Part-time study

September 2025 | 2 years

The course fee is charged per annum for 2 years. Note that fees may be subject to an increase on an annual basis - see details on our tuition fees page.

Conditional deposit

Home: Not applicable

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 good 2:2 (55% or above) or above at undergraduate level in a subject with substantial mathematical content, including Mathematics, Statistics, Physics, Engineering, Economics and Computer Science..

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 Mathematics 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 Mathematical Sciences

Postgraduate Admissions

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