MSc Statistics

MSc Statistics courses

  • MSc Statistics
  • MSc Statistics with Financial Mathematics
  • MSc Statistics with Medical Applications

Our masters courses in statistics were created to teach you the theories behind a variety of statistical techniques, and how to apply them in scenarios that professional statisticians face every day. Students graduate with the specialist modelling and analysis skills employers need to interpret the complex datasets that underpin many 21st century professions.

The University of Sheffield has more than 50 years' experience of teaching statistics at postgraduate level. Our masters degrees are currently accredited by the Royal Statistical Society (renewal in progress) so they're a great foundation for a career as a professional statistician. If you get a second-class degree or better, you automatically qualify for the RSS Graduate Statistician award – a stepping-stone to full professional membership of the RSS and Chartered Statistician status.

Effective data management is becoming of critical importance as the amount of data collected and stored, as well as the ability to analyse this data, increases.

The Future of Work: Jobs and skills in 2030

UK Commission for Employment and Skills

– Read more

Want to work while you study?
All of our MSc Statistics courses can be taken as a full-time degree, where you are based in Sheffield for one year, or as a part-time degree, where you study by distance-learning over two or three years.

Apply

To apply for this course, complete the University of Sheffield's postgraduate online application form.

Postgraduate online application form

You can find more information about the application process on the University's postgraduate webpages.

How to apply: applying essentials

Deadlines for 2019 entry

Students requiring visas: Friday 2 August
Students not requiring visas: Friday 16 August

Contact

If you have any questions about this course, contact our Postgraduate Support Officer, Fiona Maisey.

E: postgradmaths-enquiry@sheffield.ac.uk

You can also visit us throughout the year:
Postgraduate open days, visit afternoons and online chats

Admissions Tutor: Dr Kevin Walters

Pathway programme for international students

Don't meet our entry requirements?
Pre-Masters in Science and Engineering

About the course

On these courses, you can develop a detailed working knowledge of important statistical techniques and how they can be applied in a variety of different areas including social, medical and environmental science, and finance.

You'll be able learn how to use statistical modelling to analyse and draw meaningful conclusions from data, develop your programming skills using the statistical computing software R, and gain knowledge of fundamental data manipulation procedures. As you progress, you'll have the chance to improve your report-writing, presentation and group-working skills.

Each course also covers a different set of advanced or specialist topics. These include:

  • MSc Statistics: techniques in computational inference, multivariate analysis
  • MSc Statistics with Financial Mathematics: the Capital Asset Pricing Model, the Black-Scholes option pricing formula, stochastic processes
  • MSc Statistics with Medical Applications: how to apply Bayesian statistical concepts to clinical trials, statistical techniques used in epidemiology

The MSc Statistics and MSc Statistics with Medical Applications courses also highlight the ethical considerations around designing experiments involving living organisms. Our teaching is built on a deep understanding of the professional responsibilities of statisticians in society.

Course start dates 2019

Full-time students: Monday 23 September, 9.30am
Part-time students: Tuesday 24 September, 10am

All students need to in Sheffield from the start date until Friday 27th September 2019

MSc Statistics with Medical Applications student Weishan Shi shares her experiences of her course so far.

Student stories

Study part-time by distance-learning

The two- and three-year part-time versions of our MSc Statistics courses have the same content as the one-year full-time version. You will take the same exams, complete the same projects, and submit a dissertation, but you will be taught by distance-learning.

You will need to be in Sheffield for Intro Week in September of your first year, and for a few days between late May and early June each year, for your exams. The rest of the time, you are expected to spend around 20 hours each week on your studies if you are doing the two-year version of the course, and around 12-15 hours each week if you are doing the three-year version.

It is possible to transfer from the two-year version of the course to the three year version, but it much better to make sure that you can devote enough time the version you have chosen from the start. If you fail any of your modules, it is still possible to progress on to the next year, but you may need to resit the modules you have not passed, and retake the exams the following May or June.

Studying whilst working, though demanding at times, was highly beneficial as it allowed me to develop skills and knowledge which both enabled me to succeed on the masters course as well as progress within BresMed.

The technical skills gained whilst studying also mean I am able to develop my understanding of both existing and novel methods within the field of health economics, which is continually evolving.

Miranda Cooper, MSc Statistics  READ MORE

Teaching and assessment

You'll learn through a range of lectures and small projects, alongside practical classes that cover programming, modelling and data analysis. Lectures are recorded so that you can revisit them later, and distance learning students don't miss out on teaching. You'll be able to access course materials through the University of Sheffield's virtual learning environment, MOLE. The system includes discussion boards where staff and students can post and respond to questions, which distance-learning students have found particularly useful

Throughout your degree, you'll be taught by a range of experienced statisticians with a diverse range of research and teaching interests. This includes staff here in the School of Mathematics and Statistics, and specialists in the University of Sheffield's School of Health and Related Research, with expertise in medical statistics and clinical trials.

Statistics staff in the School of Mathematics and Statistics
Medical Statistics Group at the School of Health and Related Research

Assessment
The following modules are continuously assessed through ongoing project work and have no examinations:

  • Data Analysis
  • Professional Skills for Statisticians
  • Statistical Consultancy
  • Statistical Laboratory

Other modules are assessed by examinations, and coursework that contributes up to 30% to the final module mark. Your dissertation will assessed based on your final submission, as well as  progress reports and summary presentations you hand in along the way.

Dissertation

Around one-third of the course is devoted to your dissertation. Many projects involve investigating a data set, but some are based on more theoretical or methodological topics. The aim is to give you key skills and experiences to include on your CV, including:

  • planning to a deadline
  • researching background information
  • acquisition and validation of data
  • problem specification
  • carrying out of relevant analyses
  • reporting the findings

Dissertation topics are often provided by external clients, from pharmaceutical companies or sports modelling organisations. Distance learning students often come with projects designed by their employer, to be completed at the end of their final year.

Example dissertation topics (PDF)

Dissertation topics may also be based around the research interests of our staff:

Statistics Research Group: Research projects

Some of our students' dissertations have led to publications in peer-reviewed journals:

Entry requirements

For this course, we usually ask for upper second class (2:1) degree in mathematics, from a three- or four-year course, or equivalent. Applicants with high 2:2 degrees will also be considered on a case-by-case basis.

We can also accept equivalent qualifications from other countries. You can find out which qualifications we accept from your country on the University's webpages for international students.

Prospective international students: Your country

Making sure you're ready for your degree

If you don't already have a mathematics or statistics degree, try one of the following exam papers. To be ready for either of our MSc Statistics courses, you should be able to answer most questions with a small amount of revision.

If you are applying for MSc Statistics with Financial Mathematics, you should also be able to answer most of the questions in the following exam paper:

You should also make sure you are familiar with the introductory material for these courses:

Introductory material

If you don't feel ready for one of our MSc Statistics courses, we also run a Graduate Certificate in Statistics. If you do well on this course, you can progress on to one of our MSc Statistics courses.

Graduate Certificate in Statistics

International pathway programmes

If you are an international student who does not meet our entry requirements, the University of Sheffield International College offers a Pre-Masters in Science and Engineering programme. This programme is designed to develop your academic level in your chosen subject, introduce you to the study skills that will be vital to success and help with language if you need it.

Upon successful completion, you can progress to this degree at the University of Sheffield.

Pre-Masters in Science and Engineering

English Language Requirements

If you have not already studied in a country where English is the majority language, it is likely that you will need to have an English language qualification. We usually ask for:

  • International English Language Testing Service (IELTS): Overall grade of 6.5 with 6.0 in each component

You can find out whether you need to have an english language qualification, and which other English language qualifications we accept, on the University's webpages for international students.

English language requirements

The English Language Teaching Centre offers English language courses for students who are preparing to study at the University of Sheffield.

English Language Teaching Centre

Fees and funding

Up-to-date fees and funding opportunities can be found on the University of Sheffield's webpages for postgraduate students. These may include scholarships for home and international students and a 10% discount for University of Sheffield graduates.

Tuition fees

Funding your postgraduate course

Common questions

I have a good first degree in a quantitative subject (eg, economics or engineering). Will I be accepted onto any of the MSc courses?
If you have not taken many third year modules in statistics then you will not be prepared for the MSc courses. You should consider taking our Graduate Certificate in Statistics before starting one of the MSc courses.

I have a good first degree in a quantitative subject (eg, economics or engineering) and have been using statistics as part of my job. Will I be accepted onto any of the MSc courses?
This depends on the sort of statistics you have been using and how you have been using them. In this case you should contact the MSc Statistics admissions tutor to discuss whether your experiences are relevant.

Are any of the lectures recorded?
Most of the lecture courses have video recordings. They may be recordings from previous years if there has been no change of content.

I graduated many years ago so I am not sure how to get an academic reference.
With many universities it is possible to list the current Head of Department or Registrar as referee and ask them for a formal academic reference to be sent to us (preferably with the course and date of graduation). We have had references via this route even for people who graduated thirty years earlier. You should check with your university.

Will I be able to defer my offer of a place on one of the MSc courses until next year?
Yes, but only for one year. If you have already deferred for one year you cannot do so again. You will need to make a new application with updated CV and references, at least one of which should cover the past two or more years. However, any offer of funding will not be carried over for another year.

Do I need to get my degree transcripts translated in to English?
Yes.

Should I ask my referees to send you a reference or do you contact them?
You will be asked to provide contact details of two referees on the electronic application form. We will usually contact them directly. All you need to do is to ask the referee for permission to use them as a referee. Try to supply at least one academic reference.

Is there an application fee?
No.

How long will I have to wait before I get a decision on my application?
We try to get a decision to you as soon as possible. Once we have received your references we normally make a decision within two weeks. However, you should be aware that references can take a long time to come through to us.

Is the RSS Graduate Diploma a suitable entry course?
Yes. The offer we make will depend on other factors in your application but it is a recognised entry qualification.

Current modules

The modules listed below are examples from the current academic year. There may be some changes before you start your course.

Students who study part-time by distance learning take three modules each year if they are doing the two-year version of the course, and two modules each year if they are doing the three-year version of the course. Click on one of the following links to find out when you will take each module:






Compulsory modules:

Dissertation

Module leader: Dr Kostas Triantafyllopoulos

The dissertation is an extensive study giving the student the opportunity to synthesise theoretical knowledge with practical skills and giving experience of the phases of a relatively large piece of work: planning to a deadline; researching background information; acquisition and validation of data; problem specification; the carrying through of relevant analyses; and reporting, both at length through the dissertation and in summary, through, for example, a poster display. Most dissertations involve the investigation of a data set, entailing both a description of the relevant background and a report on the data analysis.

Linear Modelling

Module leader: Dr Kostas Triantafyllopoulos

The unit develops students' understanding of the general theory of linear models for regression modelling and analysing experiments, and introduces extensions to these models. Many important applications are considered, including the modelling of binary and count data, and the analysis of contingency tables and structured data. Discussion in the unit covers regression model building and model checking, multiple regression, generalised linear models, and the analysis of complete factorial experiments. It then considers mixed effects models, which are useful when the data are structured, with different levels of variation. Finally, data structures with missing parts (known as missing data) are considered in detail and relevant methods are studied.

Statistical Laboratory

Module leader: Dr Kevin Walters

This module starts in Intro Week with a speedy review of the basic background expected for the MSc. The module will then introduce students to a range of statistical and programming techniques and give practice in their implementation and interpretation using the software R. It aims to help students develop the knowledge and experience to select and use appropriate techniques for a variety of problems. The emphasis will be on practical application of techniques and knowledge of their scope rather than development of theoretical underpinnings (which will be met in other units). Areas to be covered include: exploratory data analysis, simple checks on data, density estimation, simulation, programming and optimisation.

MSc Statistics students also take:

Dependent Data

Module leader: Dr Frazer Jarvis

The unit develops concepts and techniques for the analysis of data having the complex structure typical of many real applications. The two main themes are the analysis of observations on several dependent variables, and the analysis of dependent observations made over a period of time on a single variable.

The unit begins with a practical introduction to multivariate analysis covering some of the following: Data Mining techniques, summarizing and displaying high dimensional data, dimensionality reduction, principal components, multidimensional scaling, multivariate analysis of variance and discrimination. Machine Learning approaches are also considered.

A review of repeated measures problems links to ideas of time series analysis. General techniques for the study of time series are developed, including structural descriptions, Box-Jenkins and state-space models and their fitting, and techniques for forecasting, covering local level, trend and seasonal time series. Emphasis is given to the practical implementation of the techniques using appropriate computer packages.

Inference

Module leader: Dr Miguel Juarez

This unit is largely concerned with practical statistical inference. Modern computational tools for the implementation of the frequentist and likelihood-based approaches to inference are explored, with strong emphasis placed on the use of simulation and Monte Carlo methods. Statistical theory is also developed with an introduction to the Bayesian approach to inference and decision making. Computational methods for practical Bayesian inference will also be covered.

Sampling, Design, Medical Statistics

Module leader: Dr Kevin Walters

This unit looks at the particular application area of Medical Statistics, and also considers efficient designs for the collection of data through samples, surveys and experiments. In Clinical Trials students meet some variants on mainstream theory designed to accommodate ethical constraints arising from experimenting on humans. Comparing survival patterns (of patients or industrial components) is often necessary and Survival Analysis introduces appropriate methods which handle censored data.

Implementation of techniques in standard statistical packages forms an important aspect of the unit. Sampling Theory introduces methods for obtaining samples from finite populations and conducting surveys. The impact of using different experimental designs on the statistical properties of the results will also be studied. Some standard designs will be introduced, as well as the theory required to tailor-make designs that fully satisfy the requirements of the investigations where they would be used.

Professional Skills for Statisticians

Module leader: Professor Caitlin Buck

The module helps to prepare statisticians for the workplace - in integrating technical knowledge with practical data-analysis; in communicating (in writing and orally) to non-specialists; in team-working; and through an appreciation of the professional responsibilities of a statistician. Students undertake a sequence of projects drawn from a variety of areas such as social, medical, environmental, and other scientific fields. Work on some projects is undertaken individually, and some in groups. Informal lectures are offered on: presentation skills (written and oral); team-working; personal reflection, with an emphasis on learning from experiences acquired through project work.

Statistical Consultancy

Module leader: Professor Caitlin Buck

This module builds on skills developed in 'Professional Skills for Statisticians'. Having developed skills and confidence in presentation, team-working and problem-solving, students will be challenged with a series of more demanding ‘consultancy-style’ problems. These will be open-ended and may be tackled with a variety of approaches. Students will practise conversing with ‘clients’, formulating problems statistically, identifying appropriate analysis strategies, and will develop experience and confidence as applied statisticians. There will be group and individual projects. Students will use their technical knowledge acquired from other modules, and may also need to learn new methodology independently.

MSc Statistics with Financial Mathematics students also take:

Financial Mathematics

Module leader: Dr Moty Katzman

The discovery of the Capital Asset Pricing Model by William Sharpe in the 1960's and the Black-Scholes option pricing formula a decade later mark the beginning of a very fruitful interaction between mathematics and finance. The latter obtained new powerful analytical tools while the former saw its knowledge applied in new and surprising ways. (A key result used in the derivation of the Black-Scholes formula, Ito's Lemma, was first applied to guide missiles to their targets; hence the title 'rocket science' applied to financial mathematics). This course describes the mathematical ideas behind these developments together with their applications in modern finance, and includes a computational project where students further explore some of the ideas of option pricing.

Stochastic Processes and Finance

Module leader: Dr Nic Freeman

A stochastic process is a mathematical model for a randomly evolving system. In this course we study several examples of stochastic process and analyse their behavior. We apply our knowledge of stochastic processes to mathematical finance, in particular to asset pricing and the Black-Scholes model.

Inference

Module leader: Dr Miguel Juarez

This unit is largely concerned with practical statistical inference. Modern computational tools for the implementation of the frequentist and likelihood-based approaches to inference are explored, with strong emphasis placed on the use of simulation and Monte Carlo methods. Statistical theory is also developed with an introduction to the Bayesian approach to inference and decision making. Computational methods for practical Bayesian inference will also be covered.

Dependent Data

Module leader: Dr Frazer Jarvis

The unit develops concepts and techniques for the analysis of data having the complex structure typical of many real applications. The two main themes are the analysis of observations on several dependent variables, and the analysis of dependent observations made over a period of time on a single variable.

The unit begins with a practical introduction to multivariate analysis covering some of the following: Data Mining techniques, summarizing and displaying high dimensional data, dimensionality reduction, principal components, multidimensional scaling, multivariate analysis of variance and discrimination. Machine Learning approaches are also considered.

A review of repeated measures problems links to ideas of time series analysis. General techniques for the study of time series are developed, including structural descriptions, Box-Jenkins and state-space models and their fitting, and techniques for forecasting, covering local level, trend and seasonal time series. Emphasis is given to the practical implementation of the techniques using appropriate computer packages.

Data Analysis

Module leader: Professor Caitlin Buck

The module aims to help students develop practical statistical skills - in data-analysis, in the use of different modes of communication and in group work and consultancy. The module is organised around projects drawn from practical statistical experience in a variety of application areas. Work on some projects is undertaken individually, and some in groups. Students’ work is presented in both written and oral form, with guidance from tutors. Round-table and electronic discussions of issues raised by projects and the work on them form an intrinsic part of the learning. Role-play forms a part of one of the projects.

MSc Statistics with Medical Applications students also take:

Bayesian Methods and Clinical Trials

Module leader: Dr Miguel Juarez

The module addresses two areas of statistical thinking and methodology, both important for medical applications. The first is the Bayesian approach to inference and decision-making, in which uncertainty in our knowledge is described by probabilities and combined optimally with observational information. The module introduces both the fundamental concepts of Bayesian inference and practical computational methods for implementation. The second part introduces a variety of clinical trial designs found in both commercial companies and in technology evaluation. The range is from laboratory early Phase trials, through pharmaceutical trials to evaluation of health technology, including evaluation of the economic component of trials.

Epidemiological Methods and Time Series

Module leader: Dr Kostas Triantafyllopoulos

The module aims to equip students with the knowledge and skills necessary to design, analyse and report a variety of studies used in epidemiology, including both prospective and retrospective studies. It will also introduce statistical models that can be used to understand and forecast changes in medical and epidemiological phenomena that evolve through time. The students will gain a knowledge of the strengths and limitations of different study designs and of different sources of epidemiological data. Besides describing the basic concepts of epidemiology the module will also cover some aspects of genetic epidemiology.

Sampling, Design, Medical Statistics

Module leader: Dr Kevin Walters

This unit looks at the particular application area of Medical Statistics, and also considers efficient designs for the collection of data through samples, surveys and experiments. In Clinical Trials students meet some variants on mainstream theory designed to accommodate ethical constraints arising from experimenting on humans. Comparing survival patterns (of patients or industrial components) is often necessary and Survival Analysis introduces appropriate methods which handle censored data.

Implementation of techniques in standard statistical packages forms an important aspect of the unit. Sampling Theory introduces methods for obtaining samples from finite populations and conducting surveys. The impact of using different experimental designs on the statistical properties of the results will also be studied. Some standard designs will be introduced, as well as the theory required to tailor-make designs that fully satisfy the requirements of the investigations where they would be used.

Professional Skills for Statisticians

Module leader: Professor Caitlin Buck

The module helps to prepare statisticians for the workplace - in integrating technical knowledge with practical data-analysis; in communicating (in writing and orally) to non-specialists; in team-working; and through an appreciation of the professional responsibilities of a statistician. Students undertake a sequence of projects drawn from a variety of areas such as social, medical, environmental, and other scientific fields. Work on some projects is undertaken individually, and some in groups. Informal lectures are offered on: presentation skills (written and oral); team-working; personal reflection, with an emphasis on learning from experiences acquired through project work.

Statistical Consultancy

Module leader: Professor Caitlin Buck

This module builds on skills developed in 'Professional Skills for Statisticians'. Having developed skills and confidence in presentation, team-working and problem-solving, students will be challenged with a series of more demanding ‘consultancy-style’ problems. These will be open-ended and may be tackled with a variety of approaches. Students will practise conversing with ‘clients’, formulating problems statistically, identifying appropriate analysis strategies, and will develop experience and confidence as applied statisticians. There will be group and individual projects. Students will use their technical knowledge acquired from other modules, and may also need to learn new methodology independently.

The content of our courses is reviewed annually to make sure it's up-to-date and relevant. Individual modules are occasionally updated or withdrawn. This is in response to discoveries through our world-leading research; funding changes; professional accreditation requirements; student or employer feedback; outcomes of reviews; and variations in staff or student numbers. In the event of any change we'll consult and inform students in good time and take reasonable steps to minimise disruption.

We timetable teaching across the whole of our campus, the details of which can be found on our campus map. Teaching may take place in a student’s home department, but may also be timetabled to take place within other departments or central teaching space.