Study options
- Starting in
- September 2025
- Location
- Mile End
- Fees
- Home: £12,850
Overseas: £33,500
EU/EEA/Swiss students
What you'll study
You will learn the core fundamentals of statistics and machine learning, together with the relevant software. You will also learn about applications of these methods in areas such as business, biostatistics, medical statistics and survey sampling. This programme is delivered across three semesters.
During the third semester, over the summer, you will work on a research project which will allow you to develop strong applied data science research skills.
Structure
- Six compulsory modules
- Two elective modules
- Final project and dissertation
Compulsory/Core modules
The module aims to provide students with a solid understanding of the theory and applications of the General Linear Models as used in modern Statistical Applications. This framework of models consists of a generalisation of linear regression that includes more general response variables such as binary, multinomial, ordinal, Poisson random variables amongst others where the underlying parameters or a function of them depend in linear fashion of the input variables. The module will provide an introduction to the basic techniques in these advanced topics. Including a review of linear and logistic regression and will progress onto how this model can be extended to more general random variables.
Each Applied Statistics and Data Science MSc student is required to complete a 60 credit project dissertation. A list of supervisors and projects will be provided in Semester B from which students can choose. Students will be offered the opportunity to discuss the project with potential supervisors in order to ensure an optimal match. They will then complete the Applied Statistics and Data Science MSc Project Approval Form by the end of Semester B. The module organiser will support this process and ensure that all students find a project and supervisor in Semester B. A typical MSc project dissertation consists of about 30 word-processed pages, covering a specific research-level topic in Applied Statistics and Data Science, usually requiring the student to understand, explain and elaborate on results from one or more journal articles and/or performing computation, simulations, or analysis. An MSc project may also involve collaboration with a collaborator based in industry. An MSc project should help prepare a good student for PhD research and even allow an excellent student the possibility of doing some research.
The ability to store, manipulate and display data in appropriate ways is of great importance to data scientists. This module will introduce you to many of the most widely-used techniques in the field. The emphasis of this module is primarily on the interactive use of various IT tools, rather than on programming as such, although in a number of cases you will learn how to develop short programs (scripts) to automate various tasks.
The module aims to introduce you to the Bayesian paradigm. The module will show some of the problems with frequentist statistical methods and demonstrate that the Bayesian paradigm provides a unified approach to problems of statistical inference and prediction. In the Module you will learn to make Bayesian inferences in a variety of problems, and apply Bayesian methods in real-life examples.
This module will introduce you to some of the most widely-used techniques in machine learning (ML). After reviewing the necessary background mathematics, we will investigate various ML methods, such as linear regression, polynomial regression, neural networks, classification with logistic regression, support vector machines and decision trees. The module covers a very wide range of practical applications, with an emphasis on hands-on numerical work using Python. At the end of the module, you will be able to formalise a ML task, choose the appropriate method to process it numerically, implement the ML algorithm in Python, and assess the method's performance.
This module introduces modern methods of statistical inference for small samples, which use computational methods of analysis, rather than asymptotic theory. The techniques covered in the module include non-parametric tests, bootstrap, and cross-validation. Most of these methods are now used regularly in modern business, finance, and science. Finally, the module includes the implementation of all the proposed methods with the statistics software R.
This module establishes the probability and statistics background required for students applying techniques or doing other advanced statistics Modules. The Module begins by covering the essential theoretical notions of probability and the distributions of random variables which underpin statistical methods. It then describes different types of statistical tests of hypotheses and addresses the questions of how to use them and when to use them. This material is essential for applications of statistics in psychology, life or physical sciences, business or economics.
Elective modules
This module will start by providing an overview of the field and its contemporaneous challenges with particular emphasis on ethical considerations and data confidentiality related to biomedical research. Students will also be provided with a review of the basic probabilistic and statistical techniques such as the basic probability distributions and hypothesis testing. The rest of the module will combine coverage of the following: 1. Statistical notions Including analysis of categorical data (chi-square, logistic regression) and continuous data (t-test, ANOVA) 2. Applications Data visualization, clinical trials, experimental design, survival analysis, meta-analysis and systematic reviews.
- The module will commence with an overview of the basic principles of sampling, types of surveys, their applications, and the importance of representative samples and sources of bias in surveys. - The following lessons will cover Simple Random sampling, Stratified sampling, Cluster Sampling, and Systematic Sampling - The rest of the module will focus on diagnostics for the efficacy of the techniques above, particularly covering Sampling Bias, Sampling Error, Estimation, Weighting, and Adjustment. The delivery will be centered around realistic cases and the use of R (potentially presenting Python syntax as a complement).
This module will present methods for time series analysis.These will allow the student to understand better how to use and extract information from historical business data series. In particular, the student will learn how to extract the pivotal concepts of time series data, including the trend and cyclic components of a data series, calculate the autocorrelation, learn about autoregressive and moving average models, and cointegration.The module will develop the notions around realistic business examples and an implementation of the methods will be provided using the statistic software R..
This module builds on the earlier module 'Machine Learning with Python', covering a number of advanced techniques in machine learning, such as different methods for clustering, dimensionality reduction, matrix completion, and autoencoders. Although the underlying theoretical ideas are clearly explained, this module is very hands-on, and you will implement various applications using Python in the weekly coursework assignments.
Assessment
- 67% Modules
- 33%
- 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 Applied Statistics and Data Science Project
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.
Career paths
Successful completion of this degree will provide you with opportunities to go into industry in areas such as clinical trials, Big Pharma, Big Tech, psychology and Government agencies.
The programme will also provide you with a platform to go into research or undertake a PhD.
Fees and funding
Full-time study
September 2025 | 1 year
- Home: £12,850
- Overseas: £33,500
EU/EEA/Swiss students
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.
- Scholarships and bursaries
- Postgraduate loans (UK students)
- Country-specific scholarships for international students
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 or above at undergraduate level in a Science, Technology, Engineering or Mathematics (STEM) subject.
Other education backgrounds can be considered subject to demonstrating satisfactory knowledge of mathematics or statistics.
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.