Postgraduate study

Statistics MSc

This course offers a modern advanced curriculum in statistics, providing the specific techniques and skills suitable for a professional career in statistics or as a solid basis for research in the area.
 
  
Duration
1 year full-time
Entry requirements
2:2 (or international equivalent) in mathematics or a closely related subject with substantial mathematical content
IELTS
6.0 (no less than 5.5 in each element)

If these grades are not met, English preparatory courses may be available
Start date
September
UK/EU fees
£9,450 - Terms apply
International fees
£18,675 - Terms apply
Accreditation
Royal Statistical Society
Campus
University Park Campus
School/department
 

 

Overview

Optional topics typically include generalised linear models, Markov Chain Monte Carlo, the bootstrap, multivariate analysis, spatial statistics, time series and forecasting, multilevel models, stochastic finance, together with shape and image analysis.

 Key facts

  • This course is founded by the work being carried out in the Statistics and Probability research group
  • The School of Mathematical Sciences is one of the largest and strongest mathematics departments in the UK, with over 70 full-time academic staff
  • The Research Excellence Framework (REF) 2014 results place the School in the top 10 nationally within Mathematical Sciences for 'research power' and 'research quality'; with 32% of its research recognised as world-leading and a further 56% as internationally excellent
  • The research environment was classified as 75% world-leading in vitality and sustainability, with the remaining 25% internationally excellent, reflecting the outstanding setting the school provides for its academic staff as well as its postdoctoral and postgraduate researchers
  • Ranked 8th in the UK for three subject areas within the School, namely pure mathematics, applied mathematics, statistics and operational research
  • This course is accredited by the Royal Statistical Society
  • The school scored 87% for student satisfaction in the National Student Survey, 2018
 

 Academic English preparation and support

If you require additional support to take your language skills to the required level, you may be able to attend a presessional course at the Centre for English Language Education, which is accredited by the British Council for the teaching of English in the UK.

Students who successfully complete the presessional course to the required level can progress to postgraduate study without retaking IELTS or equivalent. You could be eligible for a joint offer, which means you will only need to apply for your visa once.

 

Full course details

How you'll be taught

MSc Statistics is intended for students with a degree in mathematics (or a related subject with a substantial mathematical content). Some knowledge of probability and statistics would be helpful to start the course.

The course is:

  • taken full-time over one year
  • made up of compulsory and optional modules to give you the flexibility to study your topics of interest
  • comprises 180 credits, split across 120 credits’ worth of taught modules and a 60-credit research project
  • taught mostly through lectures, backed up with smaller seminar groups which are used to revisit more complex topics

You will typically be assessed by an examination or coursework at the end of the semester in which a given module is taught.

The course is designed such that the module 'Fundamentals of Statistics' is either a pre- or a co-requisite for all other modules offered in the course, whereas the recommended books to meet the prerequisite for this module are:

  • Ross, S.M. (1976) A First Course in Probability (or any of the newer editions - most recent is the 8th edition, 2010), Collier-MacMillan
  • Mann, P.S. (2010) Introductory Statistics, John Wiley & Sons

Dissertation

Over the summer period towards the end of the course, you will undertake a maths dissertation of 15,000-25,000 words.

In this module, a substantial mathematical investigation will be carried out on a topic in statistics (stats) or probability. The study will be largely self-directed, although a supervisor will provide oversight and input where necessary.

The maths topic will be chosen by agreement between yourself and your supervisor. The topic could be based on the statistical analysis of a substantial dataset, an investigation into 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.

 
 

Modules

Core modules

Fundamentals of Statistics

The purpose of this course is to provide the initial underpinning of the fundamentals of statistical inference, linear regression models, probability techniques and Markov chains.

There will be in-depth study of more advanced statistical inference theory, and hands-on experience of modern statistical computing software

 

Statistics Dissertation

The purpose of this course is to broaden and deepen the students' knowledge and understanding of statistics or probability by carrying out a detailed and substantial investigation.

The study will be largely self-directed, although a supervisor will provide oversight and input where necessary. The topic will be chosen by agreement between the student and supervisor.

The topic could be based on the statistical analysis of a substantial dataset, an investigation into 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

Group one

You may choose a minimum of two of the following: 

  • Applied Multivariate Statistics

This course is concerned with 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 factor analysis, methods of clustering and multidimensional scaling.

 

Applied Statistical Modelling

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 course 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.

Students will be trained in the use of an appropriate high-level statistical package.

 

Stochastic Financial Modelling 

In this module the concepts of discrete time Markov chains are extended and used 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. These include risk-neutral measures and Brownian motion. The capital asset pricing model is described and two Nobel Prize winning theories are obtained: the Markowitz mean-variance efficient frontier for portfolio selection and the Black-Scholes formula for arbitrage-free prices of European type options on stocks.

Students will gain experience of a topic of considerable contemporary importance, both in research and in applications.

A project will be undertaken which will involve independent reading, and a written report.

 

Time Series and Forecasting

This course 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 described. Techniques for estimating the parameters of a model, assessing its fit and forecasting future values will be developed.

Students will gain experience of using a statistical package and interpreting its output.

The course will cover:

  • concepts of stationary and non-stationary time-series;
  • philosophy of model building in the context of time series analysis;
  • simple time series models and their properties;
  • the model identification process;
  • estimation of parameters;
  • assessing the goodness of fit;
  • methods for forecasting;
  • use of a statistical package.
 


Group two

And a minimum of 20 credits from this group, but students may not take both Statistical Machine Learning and Data Analysis and Modelling.

Computational Statistics

The increase in speed and memory capacity of modern computers has dramatically changed their use and applicability for complex statistical analysis.

This course 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 to be covered include a selection from:

  • simulation methods
  • Markov chain Monte Carlo methods
  • the bootstrap and nonparametric statistics
  • statistical image analysis
  • wavelets

Students will gain experience of using a statistical package and interpreting its output.

 

Data Analysis and Modelling

This course involves the application of probability and statistics to a variety of practical, open-ended problems, typical of those that statisticians encounter in industry and commerce.

Specific projects are tackled through workshops and student-led group activities. The real-life nature of the problems requires students to develop skills in model development and refinement, report writing and teamwork.

Students will have an opportunity to apply a variety of statistical methods and knowledge.

 

Statistical Machine Learning

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 course builds on principles of statistical inference and linear regression to introduce a variety of methods of regression and classification, trade-off, and on methods to measure and compensate for overfitting.

The learning approach is hands on, with students using R to tackle challenging real world machine learning problems.

 

The modules we offer are inspired by the research interests of our staff and as a result may change for reasons of, for example, research developments or legislation changes. This list is an example of typical modules we offer, not a definitive list.

 
 

Funding

UK/EU students 

Tuition fees

Information on current course tuition fees can be found on the finance pages.

Graduate School

The Graduate School provides more information on internal and external sources of postgraduate funding.

International students 

Tuition fees

Information on current course tuition fees can be found on the finance pages.

School scholarships for UoN UK alumni

For 2019/20 entry, 10% alumni scholarships may be offered to former University of Nottingham graduates who have studied at the UK campus.

Government loans for masters courses

The Government offers postgraduate student loans for students studying a taught or research masters course. Applicants must ordinarily live in England or the EU. Student loans are also available for students from Wales, Northern Ireland and Scotland.

International and EU students

Masters scholarships are available for international students from a wide variety of countries and areas of study. You must already have an offer to study at Nottingham to apply. Please note closing dates to ensure your course application is submitted in good time.

Information and advice on funding your degree, living costs and working while you study is available on our website, as well as country-specific resources.

 
 

Careers and professional development

The programme will provide you with specific techniques and skills suitable for a professional career in statistics or as a solid basis for research in statistics. There is great demand for people with postgraduate qualifications in this area.

Some useful information can be found at The Royal Statistical Society website.

Average starting salary and career progression

In 2017, 100% of postgraduates in the school who were available for employment had secured work or further study within six months of graduation. The average starting salary was £30,800 with the highest being £60,000.*

* Known destinations of full-time home postgraduates 2016/17. Salaries are calculated based on the median of those in full-time paid employment within the UK.

Career prospects and employability

The University of Nottingham is consistently named as one of the most targeted universities by Britain’s leading graduate employers.

Ranked in the top 10 in The Graduate Market 2013-2017 – High Fliers Research

Those who take up a postgraduate research opportunity with us will not only receive support in terms of close contact with supervisors and specific training related to your area of research, you will also benefit from dedicated careers advice from our Careers and Employability Service

Our  Careers and Employability Service offers a range of services including advice sessions, employer events, recruitment fairs and skills workshops – and once you have graduated, you will have access to the service for life.

 
 
 

Disclaimer
This online prospectus has been drafted in advance of the academic year to which it applies. Every effort has been made to ensure that the information is accurate at the time of publishing, but changes (for example to course content) are likely to occur given the interval between publishing and commencement of the course. It is therefore very important to check this website for any updates before you apply for the course where there has been an interval between you reading this website and applying.

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Dr Huiling Le
School of Mathematical Sciences
The University of Nottingham
University Park
Nottingham
NG7 2RD
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