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Course overview

Are you considering a career as a professional statistician in industry? This course will provide you with the necessary advanced knowledge and skills. This masters course differs from the Statistics MSc, by providing specialised training in probability models, suitable for roles in business and finance. 

You'll study two compulsory modules in Fundamentals of Statistics and Stochastic Models. They provide the basis for the remaining optional modules. These include, amongst others, Mathematical Finance and Statistical Machine Learning.

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 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 Research group, or one of our industry partners, such as Capital One.

Why choose this course?

Flexible programme

with a broad range of modules influenced by our research expertise

International research

modules taught by experts in infectious disease modelling, analysis of object data and Bayesian computation

Research Excellence Framework 2014

Expand your network

interact with other MSc students

 

Analytical thinking

develop skills to think logically and critically, gain competence in statistical software

Unique MSc in the UK

allows you to specialise in statistics with particular focus on applied probability.

Based on Find a Masters search May 2020.

Course content

The course is split between core and optional modules.

You will study the compulsory modules in Fundamentals of Statistics and Stochastic Models. This will provide you with the knowledge and skills required to complete your chosen optional modules during the rest of the year.

On completion of your optional modules, you will prepare a compulsory written research dissertation. You will be given one-to-one support from your supervisor who will offer advice and guidance during your dissertation.

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

* Please note, you may not take both Statistical Machine Learning and Uncertainty Quantification as optional modules.

 

Modules

Core modules

Fundamentals of Statistics

In this module you will explore the fundamental principles and techniques underlying modern statistical modelling and data analysis. The course will cover a 'common core' consisting of statistical concepts and methods, linear models, probability techniques and Markov chains. You will gain experience of using a statistical package and interpreting its output.

In addition, you will study more advanced material concerned with the two main theories of statistical inference, namely classical (frequentist) inference and Bayesian inference. Topics such as sufficiency and best-unbiased estimators are explored in detail. There is special emphasis on the exponential family of distributions. Topics in Bayesian inference include basic ingredients (prior, likelihood and posterior), conjugacy, vague peior knowledge, marginal and predictive inference, decision theory, normal inverse gamma inference, and categorical data.

Statistics Dissertation

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.

Stochastic Models

In this module the ideas of discrete-time Markov chains are extended to include more general discrete-state space stochastic processes evolving in continuous time and applied to a range of stochastic models for situations occurring in the natural sciences and industry. You will be introduced to Poisson processes and birth-and-death processes. This is followed by more extensive studies of epidemic models and queueing models, and introductions to component and system reliability. 

Optional modules

You choose three optional modules from one of the following:

Applied Multivariate Statistics

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.

Computational Statistics

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.

Statistical Machine Learning

This module is a topic at the interface between statistics and computer science, concerning models that can adapt to and make predictions based on data. This module builds on the principles of statistical inference and linear regression to introduce a variety of methods of clustering, dimension reduction, regression and classification.

Much of the focus is on the bias-variance trade-off and on methods to measure and compensate for overfitting. The learning approach is hands-on; you will be using R extensively in studying contemporary statistical machine learning methods, and in applying them to tackle challenging real-world applications.

Stochastic Financial Modelling

The aim of the module is to provide an introduction to probabilistic and stochastic modelling for investment strategies, and for the pricing of financial derivatives in risky markets. The probabilistic ideas that underlie the problems of portfolio selection, and of pricing and hedging options, are introduced. You will gain experience of a topic of considerable contemporary importance, both in research and in applications. You will undertake a project which will involve independent reading, and a written report.

Time Series and Forecasting

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.

Uncertainty Quantification

This module aims to provide a comprehensive introduction to the key concepts in the field of uncertainty quantification. You will be introduced to a variety of techniques which are useful in UQ and will focus on a more in-depth study of selected application areas. The module will cover:

  • sensitivity analysis
  • Bayesian approaches to inverse problems
  • spectral decomposition methods
  • data assimilation and filtering
  • interpolation and approximation using orthogonal polynomials
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 Tuesday 30 March 2021.

Learning and assessment

How you will learn

We are preparing your tutorials, laboratory classes, workshops and seminars so that you can study and discuss your subjects with your tutors and fellow students in stimulating and enjoyable ways. While we will keep some elements of online course delivery, particularly while Covid-19 restrictions remain in place or where this enhances course delivery, teaching is being planned to take place in-person wherever possible. This will be subject to government guidance remaining unchanged.

We will use the best of digital technologies to support both your in-person and online teaching. We will provide live, interactive online sessions, alongside pre-recorded teaching materials so that you can work through them at your own pace. While the mix of in-person and digital teaching will vary by course, we aim to increase the proportion of in-person teaching in the spring term.

  • Lectures
  • Workshops
  • Computer labs

Some modules will be taught alongside students from other courses.

How you will be assessed

All assessments in the 2021/22 academic year will be delivered online unless there is a professional accreditation requirement or a specific need for on-campus delivery and in-person invigilation.

  • 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 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. All modules are typically delivered by Professors, Associate and Assistant Professors. Additional support in small group and practical classes may involve PhD students and post-doctoral researchers.

The majority of your lecturers and tutors will be based within the mathematics 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 2021 entry.

Undergraduate degreeA high 2:2 in mathematics or a closely related subject with substantial mathematical content.
Portfolio

An initial familiarity with probability at intermediate level will be assumed.

Applying

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

How to apply

Fees

Qualification MSc
Home / UK £11,000 per year
International £22,000 per year

If you are a student from the EU, EEA or Switzerland starting your course in the 2021/22 academic year, you will pay international tuition fees.

This does not apply to Irish students, who will be charged tuition fees at the same rate as UK students. UK nationals living in the EU, EEA and Switzerland will also continue to be eligible for ‘home’ fee status at UK universities until 31 December 2027.

For further guidance, check our Brexit information for future students.

Additional costs

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 will be given £5 worth of printer credits a year. You are welcome to buy more credits if you need them. It costs 4p to print one black and white page.

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

For 2021/22 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.

More than 1,500 employers advertise graduate jobs and internships through our online vacancy service. We host regular careers fairs, including specialist fairs for different sectors.

Graduate destinations

Alongside their statistical knowledge and skills in probability, our graduates leave Nottingham with valuable skills in:

  • logical thinking
  • problem-solving
  • data analysis and manipulation

Statisticians are required to work in many sectors including banking, education, finance, healthcare, sport and transport.

Previous graduates work as:

  • Analysis officer
  • Business analyst
  • Pricing model analyst

They have taken roles in organisations including Capital One, HMRC, and the Lowell Group.

Career progression

97.5% of postgraduates from the School of Mathematical Sciences secured graduate level employment or further study within 15 months of graduation. The average annual salary for these graduates was £28,131.*

* HESA Graduate Outcomes 2020. The Graduate Outcomes % is derived using The Guardian University Guide methodology. The average annual salary is based on graduates working full-time within the UK.

Two masters graduates proudly holding their certificates
" I'm Theo Kypraios, I teach the Data, Analysis and Modelling module on the Statistics MSc. My research is concerned with developing new computational statistical methodology for Bayesian statistical inference and model selection for high-dimensional complex data. The application area that I have mostly worked on is infectious disease modelling, but I have also worked on other areas such as ecology, biosciences and quantum statistics in collaboration with colleagues from these fields. "
Dr Theo Kypraios is an Associate Professor in Statistics and Head of the Statistics and Probability research group.

Related courses

The University has been awarded Gold for outstanding teaching and learning (2017/18). Our teaching is of the highest quality found in the UK.

The Teaching Excellence Framework (TEF) is a national grading system, introduced by the government in England. It assesses the quality of teaching at universities and how well they ensure excellent outcomes for their students in terms of graduate-level employment or further study.

This content was last updated on Tuesday 30 March 2021. 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.