Triangle

Course overview

The Statistical Science MSc is a part-time distance learning programme, offering you the flexibility to study your way. You will develop the advanced techniques and skills required to be a successful statistician in the 21st century.

As the data we generate increases, so does the global demand for analysts who can apply modern statistical methods to make sense of it. The MSc ensures you will gain the skills to go from exploring a data set, to modelling and analysing the data, through to presenting your findings in a variety of ways.

You will develop:

  • essential statistical knowledge
  • analytical skills
  • computational expertise
  • interpretive and communicative skills

Teaching is provided through course materials including lecture notes, digital recordings, training videos and practical computer sessions. You have the flexibility to look at these in your own time, meaning you can complete the masters around other commitments. We will support you throughout the degree with live online sessions using Microsoft Teams (or similar) and virtual computer labs. This gives you the opportunity to ask any questions you have about the module content.

You will study the core statistical concepts of inference and modelling. As you progress, you will cover advanced topics in machine learning, multivariate statistics and time series. These topics will develop your understanding of modern statistical techniques, leading to a dissertation which lets you demonstrate the skills you have gained and develop your ability to study independently. You will be supported by expert lecturers and leading statistical experts and researchers throughout the MSc.

Continued Professional Development (CPD)

If you are already working in a statistics-related role, this MSc will enhance your existing knowledge and skills. You will formalise and consolidate your learning and practical skills through carefully chosen modules.

All of the modules are available as standalone units to enhance your career development. Some examples include:

  • Frequentist Statistical Inference - learn the fundamentals of statistical inference and the computational implementation of inferential methods
  • Statistical Modelling of Discrete and Survival - explore extensions of linear models which enable the analysis of data in a wide range of scenarios
  • Statistical Machine Learning - use modern methods, combining statistics and computation, to make predictions for real-life data sets

Why choose this course?

Study your way

flexibility to study around your current work or lifestyle

International research

experts in infectious disease modelling, data-driven modelling and Bayesian computation

Expert teaching

from a prestigious UK Russell Group university

Analytical thinking

develop skills to think logically and critically

Hands-on experience

of using statistical software including R

Course content

Course of study

The course consists of 120 credits of taught modules over 3 semesters as follows:

Year 1 Semester 1; September – January and Semester 2; February – June

Year 2 Semester 1; September – January

Each semester consists of 40 credits of taught modules (two modules).

Dissertation

60 credit dissertation to be completed after taught component during February – September in Year 2.

Study support

You will be allocated a personal tutor. This is someone who works closely to support you throughout the programme. Each module is taught by a module lecturer; an expert academic member of staff who will provide support throughout the module. Dissertation support and one-to-one sessions with your supervisor will be provided.

Throughout the MSc there are opportunities to chat with fellow students and study together online as you progress through the course.  You can share your ideas and support each other in any topics in which you may be struggling.

Modules

Core modules

Foundations of Statistics 20 credits

In this course the fundamental principles and techniques underlying modern statistical and data analysis will be introduced. The course will cover the core foundations of statistical theory consisting of:

  • probability distributions and techniques;
  • statistical concepts and methods;
  • linear models

The course highlights the importance of computers, and in particular, statistical packages, in performing modern statistical analysis. You will be introduced to the statistical package R as a statistical and programming tool and will gain hands-on experience in interpreting and communicating its output.

Frequentist Statistical Inference 20 credits

This module is concerned with frequentist (classical/frequentist) statistical inference, both its theory and its applications and builds on the fundamental ideas of statistics introduced in the module “Foundations of Statistics”.

The following topics are explored and the Delta Method is also presented:

  • maximum likelihood estimation
  • properties of estimators
  • confidence intervals
  • likelihood ratio tests

There is emphasis on the exponential family of distributions, which includes many standard distributions such as the normal, Poisson, binomial and gamma. You will also explore how computers can be utilised to perform statistical inference for non-standard (i.e. analytically intractable) problems by applying innovative statistical and numerical methods.

Optimisation methods, the bootstrap algorithm and simulation techniques including Monte Carlo methods will be introduced in relation to problems of statistical inference. You will gain experience of linking the underlying statistical concepts to practical applications of the methodology.

You will gain experience of using statistical software and interpreting its output.

Statistical Modelling of Discrete and Survival Data 20 credits

This module develops the theory of the generalised linear model and its practical implementation. It builds upon and extends the linear model introduced in the Foundations of Statistics module.

The teaching extends the understanding and application of statistical methodology to the analysis of discrete (count and binary) data and survival models, which frequently occur in diverse applications. You will gain experience of using statistical software to perform exploratory data analysis and to apply generalised linear model methodology to a wide range of applications.

You will develop key statistical skills in interpreting and communicating their statistical analysis.

Bayesian Data Analysis: Theory, Applications and Computational Methods 20 credits

This module is concerned with the second main theory of statistical inference, Bayesian inference. It complements the frequentist statistical approach introduced in Statistical Inference.

This module will provide a full description of Bayesian analysis and cover popular models, such as the normal distribution and inference for categorical data.

Topics include:

  • prior elicitation
  • conjugate models
  • marginal and predictive inference
  • hierarchical models and model choice

Well known classical procedures, such as point estimation and confidence intervals, will be compared with their Bayesian counterparts.

You will also explore how computers allow the easy implementation of standard, but computationally intensive, statistical methods such as Markov chain Monte Carlo methods, to obtain samples from a posterior distribution. 

You will gain experience of linking the underlying statistical concepts to practical applications of the methodology and benefit from hands-on experience of using statistical software and interpreting its output.

Exit awards

There is an exit point at the end of the taught modules in year one.

If you leave after successfully completing 60 credits in year one, you will gain a Postgraduate Certificate qualification (PGCert).

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 17 June 2022.

Core modules

Statistical Machine Learning 20 credits

Statistical Machine Learning is a topic at the interface between statistics and computer science. It concerns models that can adapt to and make predictions based on data.

The module builds on principles of statistical inference and linear regression developed in Foundations of Statistics and Statistical Inference 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 situations.

Multivariate and Time Series Analysis 20 credits

This module is concerned with modelling and analysing data with structural dependence. We will cover two main topics:

1. time series models for analysing data that arise sequentially in time

2. multivariate data analysis in which the response is a vector of random variables rather than a single random variable

Several commonly occurring time series 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. You will gain experience of using a statistical package and interpreting its output.

For multivariate data analysis, 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 estimating and testing, based on the multivariate normal distribution
  • classification of observation vectors into subpopulations using a training sample
Statistics Dissertation 60 credits

In this course a substantial investigation will be carried out on a topic in Statistics. 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 you and your supervisor. The topic could be based on the statistical analysis of a substantial dataset or an investigation into statistical methodology. It is expected that projects will contain an element of statistical computing.

Exit awards

There is an exit award at the end of the taught modules in year two.

If you leave after successfully completing only the year one and two taught modules, you will gain a Postgraduate Diploma (PGDip).

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 17 June 2022.

Learning and assessment

How you will learn

  • Independent study
  • Distance learning materials
  • Lectures
  • eLearning

You will be taught through modules specifically designed for distance learning. These will be supported with interactive learning materials including:

  • interactive lecture notes
  • explanatory videos
  • computer worksheets
  • exercises
  • quizzes

You will be able to access these learning resources through our online teaching platform. This is available on demand so you can study at a time that suits you.

Each module will be led by an academic member of staff who will provide the learning resources and lead the teaching sessions. Additional support will be provided by PhD students and post-doctoral researchers.

You will be supported in your independent learning by online tutorials with the lecturer and other students. 

How you will be assessed

  • Examinations
  • Coursework
  • Dissertation
  • Short project

Exams will take place at the university, or an approved test centre.

Examinations will take place at the end of each semester. In Year one when there are two exams at the end of each semester these will be scheduled to minimise travelling for students.

In year two there will be one exam at the end of Semester 1.

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 course is designed to allow flexibility to study when is convenient for you. Each module will have a small number of timetabled meeting sessions. This enables you to meet virtually with the lecturer and other students on the masters.

Each module will have allocated time slots and forums to allow you to speak directly with the lecturer too.

Entry requirements

All candidates are considered on an individual basis and we accept a broad range of qualifications. The entrance requirements below apply to 2023 entry.

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

Some prior knowledge of statistics would be helpful but not essential to start the course.

Applying

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

How to apply

Fees

UK fees are set in line with the national UKRI maximum fee limit. We expect fees for 2023 entry to be confirmed in August 2022.

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.

Books

Non-essential (materials complete) but might be helpful. A wide range of statistics books can be accessed electronically through the library.

Equipment

A computer is essential to access the learning materials, use statistical software and prepare reports for assignments. A laptop is preferable.

Travel

Travel will be required to attend the University of Nottingham or an approved test centre for examinations.

Assessment costs

There will be additional assessment costs if examinations are taken at a venue other than a University of Nottingham campus. The costs will be venue specific.

 

 

Funding

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.

Graduate destinations

Alongside their statistical knowledge, graduates will leave the course with valuable skills in:

  • logical thinking
  • problem-solving
  • data analysis and manipulation
  • communicating statistical findings

Graduates go on to a wide range of careers. Some enter roles that have a direct correlation to their degree, including banking, education and finance. Others utilise their transferable skills in sectors such as healthcare, sport and transport.

An MSc in statistics also opens up opportunities in data science.

Career progression

96.4% 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 £27,426.*

* 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 am the Director of the MSc in Statistical Science and module lecturer for the Foundations of Statistics module. My research is focussed on the statistical modelling of the spread of infectious diseases. The importance of statistics and data has never been greater valued by society with applications in all areas of life. It is an exciting time to be a statistician with the wide range of career options available. "

Related courses

This content was last updated on Friday 17 June 2022. 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.