Triangle

Course overview

Are you considering a career as a professional statistician in industry? Do you want to follow a career in machine learning?

This course will provide you with the necessary advanced knowledge and skills.  This masters course differs from the Statistics MSc, by providing a particular focus on statistical machine learning to help you understand and build methods that are used in everyday life. For instance, machine learning algorithms are used in mobile phones, self-driving cars, digital health, protein engineering, adaptive clinical trials and lifelong learning in robotics.

Well-qualified statisticians and data scientists are in demand world-wide as the amount of digital data we generate increases. Employment opportunities are broad in sectors such as:

  • pharmaceuticals
  • finance
  • computing and AI
  • business analytics
  • healthcare
  • government policy
  • social media and technology

Course structure

In the Autumn semester you will study the core fundamentals of statistics and data science. This will provide the basis for the optional modules. This will provide a firm foundation in statistical machine learning for the Spring semester which includes optional modules covering topics in time series and multivariate statistics.

You will develop advanced statistical techniques. This will enable you to test theories, learn how to interpret large and complex data, and to extract relevant insights from it. The masters will enable you to develop computing skills in using R too. The course can lead to careers in data science, healthcare and the digital economy.

In the summer you'll complete a dissertation. This will be in collaboration with staff from the school's internationally recognised Statistics and Probability Section, or one of our industry partners, such as Capital One.

Please note: this is the proposed course structure for 2025, the modules are currently being finalised so please check here or contact the Course Director before submitting your application.

Research Excellence Framework 2021

  • 97% of our research outputs are rated as 'world-leading' or 'internationally excellent'
  • The school was placed in the top 3 for quality of research environment across all mathematical sciences units in the UK
  • 100% of the impact from the school is rated as either ‘world-leading’ or ‘internationally excellent’

This is highlighted in our commitment to attracting bright minds and inspiring academics to conduct mathematical research throughout the department.

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Why choose this course?

Analytical thinking

develop skills to think logically and critically, become competent using statistical software including R

Scholarships available

to help fund your postgraduate course

Ranked top 3 in the UK

for research environment

REF 2021

Flexible programme

with a broad range of modules influenced by our research expertise

Machine learning

Study advanced topics in data science

Expand your network

interact with other MSc students

 

Analytical thinking

develop skills to think logically and critically, become competent using statistical software including R

Scholarships available

to help fund your postgraduate course

Ranked top 3 in the UK

for research environment

REF 2021

Flexible programme

with a broad range of modules influenced by our research expertise

Machine learning

Study advanced topics in data science

Expand your network

interact with other MSc students

 

Course content

The course is split between core and optional modules.

During the year you will study a total of 180 credits. 120 credits worth of taught modules and the 60-credit dissertation.

Modules

Core modules

In this module, the fundamental principles and techniques underlying modern statistical and data analysis will be introduced. You will gain experience in using a statistical package and interpreting its output. The course will cover a 'common core' consisting of:

  • statistical concepts and methods
  • linear models
  • probability techniques
  • Markov chains

You will explore the two main theories of statistical inference, namely classical (frequentist) inference and Bayesian inference.

You cover topics including:

  • sufficiency
  • estimating equations
  • likelihood ratio tests
  • best-unbiased estimators

There is special emphasis on the exponential family of distributions, which includes many standard distributions such as the normal, Poisson, binomial and gamma.

The module will give you hands-on experience of using statistical software and interpreting its output, as well as short-report writing.

Machine Learning is a topic at the interface between statistics and computer science that concerns models that can adapt to and make predictions based on data. This module builds on principles of statistical inference and linear regression. It introduces a variety of methods of regression and classification, trade-off, and on methods to measure and compensate for overfitting.

You will benefit with hands-on learning using computational methods to tackle challenging real world machine learning problems.

You will work on a substantial investigation on a topic in statistics or probability. The study will be largely self-directed, although a supervisor will provide oversight and input where necessary. The topic could be based on the statistical analysis of a substantial dataset, an investigation into the statistical methodology or an investigation into a topic of applied probability or probability theory. It is expected that most projects will contain an element of statistical computing.

Optional modules

During this module you will explore the analysis of multivariate data, in which the response is a vector of random variables rather than a single random variable. A theme running through the module is that of dimension reduction. Key topics to be covered include: principal components analysis, whose purpose is to identify the main modes of variation in a multivariate dataset; modelling and inference for multivariate data, including multivariate regression data, based on the multivariate normal distribution; classification of observation vectors into subpopulations using a training sample; canonical correlation analysis, whose purpose is to identify dependencies between two or more sets of random variables. Further topics to be covered include methods of clustering and multidimensional scaling.

This module explores how computers allow the easy implementation of standard, but computationally intensive, statistical methods and also explores their use in the solution of non-standard analytically intractable problems by innovative numerical methods. Particular topics covered include a selection from simulation methods, Markov chain Monte Carlo methods, the bootstrap and nonparametric statistics, statistical image analysis, and wavelets. You will gain experience of using a statistical package and interpreting its output.

We consider modern machine learning tools including Gaussian processes and deep neural networks within a framework for building and training surrogates for computer models derived from Differential Equations (DEs).

We employ these tools within the study of advanced techniques for uncertainty propagation and parameter inference in DEs. Uncertainty propagation techniques comprise Monte Carlo methods, stochastic colocation and stochastic Galerkin.

Modern techniques for parameter inference for DEs are covered from the deterministic as well as the Bayesian perspective.

This module will provide a general introduction to the analysis of data that arise sequentially in time. Several commonly occurring models will be discussed and their properties derived. Methods for model identification for real-time series data will be considered. Techniques for estimating the parameters of a model, assessing its fit and forecasting future values will be developed. You will gain experience of using a statistical package and interpreting its output.

This module extends the theoretical aspects of statistical inference by developing the theory of the generalised linear model and its practical implementation. Initially, designing of experiments in order to explore relationship between factors and a response is viewed within the context of Linear models.

The module then extends the understanding and application of statistical methodology established in previous courses to the analysis of discrete data and survival, which frequently occur in diverse applications. You will be trained in the use of an appropriate high-level statistical package.

The above is a sample of the typical modules we offer but is not intended to be construed and/or relied upon as a definitive list of the modules that will be available in any given year. Modules (including methods of assessment) may change or be updated, or modules may be cancelled, over the duration of the course due to a number of reasons such as curriculum developments or staffing changes. Please refer to the module catalogue for information on available modules. This content was last updated on Friday 07 March 2025.

Due to timetabling availability, there may be restrictions on some module combinations.

Learning and assessment

How you will learn

  • Lectures
  • Workshops
  • Computer labs
  • Group study
  • Independent study
  • Reports
  • Presentations
  • Problem classes

You will broaden and deepen your knowledge of mathematical ideas and statistical techniques using a wide variety of different methods of study. 

Some modules will be taught alongside students from other courses.

How you will be assessed

  • Coursework
  • Dissertation
  • Examinations
  • Project work

You will be awarded the Master of Science Degree provided you have successfully completed the taught stage by achieving a weighted average mark of at least 50% with no more than 40 credits below 50% and no more than 20 credits below 40%.

You must also achieve a mark of at least 50% in the dissertation.

Contact time and study hours

The number of formal contact hours varies depending on the optional modules you are studying. As a guide, in the Autumn and Spring semesters you will typically spend around 14 hours per week in lectures.

You will work on your research project between June and September, usually based at the University.

Teaching is provided by academic staff within the School of Mathematical Sciences. The majority of modules are typically delivered by Professors, Associate and Assistant Professors. Additional support in small group and computer lab sessions may involve PhD students and post-doctoral researchers.

The majority of your lecturers and tutors will be based within the mathematical sciences building. This means if you need to get in touch with them during office hours, they can be contacted easily as they are close by.

Entry requirements

All candidates are considered on an individual basis and we accept a broad range of qualifications. The entrance requirements below apply to 2025 entry.

Undergraduate degreeA high 2:2 (55% or international equivalent) Honours degree in mathematics or a closely related subject with substantial mathematical content. A strong mathematical background is essential.
Portfolio

Applicants should have a solid background in mathematics including calculus, linear algebra, probability and statistics at degree level.

We may ask you to provide detailed syllabi, including module descriptions, of all mathematics and statistics modules that are a part of your degree. 

Applying

Our step-by-step guide covers everything you need to know about applying.

How to apply

Fees

Qualification MSc
Home / UK £13,450
International £27,150

Additional information for international students

If you are a student from the EU, EEA or Switzerland, you may be asked to complete a fee status questionnaire and your answers will be assessed using guidance issued by the UK Council for International Student Affairs (UKCISA) .

These fees are for full-time study. If you are studying part-time, you will be charged a proportion of this fee each year (subject to inflation).

Additional costs

All students will need at least one device to approve security access requests via Multi-Factor Authentication (MFA). We also recommend students have a suitable laptop to work both on and off-campus. For more information, please check the equipment advice.

As a student on this course, we do not anticipate any extra significant costs, alongside your tuition fees and living expenses.

Printing

Due to our commitment to sustainability, we don’t print lecture notes but these are available digitally. You are welcome to buy print credits if you need them.

Books

You should be able to access most of the books you’ll need through our libraries, though you may wish to purchase your own copies which you would need to factor into your budget.

Computers

Personal laptops are not compulsory as we have computer labs that are open 24 hours a day but you may want to consider one if you wish to work at home.

Funding

School scholarships for UoN international alumni

We invite our alumni to continue with us for masters study within The School of Mathematical Sciences. For 2025/26 entry, 10% alumni scholarships may be offered to former University of Nottingham international graduates who have studied at the UK campus.

There are many ways to fund your postgraduate course, from scholarships to government loans.

We also offer a range of international masters scholarships for high-achieving international scholars who can put their Nottingham degree to great use in their careers.

Check our guide to find out more about funding your postgraduate degree.

Postgraduate funding

Careers

We offer individual careers support for all postgraduate students.

Expert staff can help you research career options and job vacancies, build your CV or résumé, develop your interview skills and meet employers.

Each year 1,100 employers advertise graduate jobs and internships through our online vacancy service. We host regular careers fairs, including specialist fairs for different sectors.

International students who complete an eligible degree programme in the UK on a student visa can apply to stay and work in the UK after their course under the Graduate immigration route. Eligible courses at the University of Nottingham include bachelors, masters and research degrees, and PGCE courses.

Two masters graduates proudly holding their certificates

Related courses

This content was last updated on Friday 07 March 2025. Every effort has been made to ensure that this information is accurate, but changes are likely to occur given the interval between the date of publishing and course start date. It is therefore very important to check this website for any updates before you apply.