MSc Applied Statistics

Prefer to study this course online? We also offer our MSc Applied Statistics (online).

Key facts

  • Start date: September
  • Study mode and duration: MSc: 12 months full-time
  • Accreditation: On successful completion of the MSc, students may be eligible for GradStat status

Study with us

  • a conversion course for those with a background in a broad range of disciplines 
  • gain skills in problem-solving, the analysis and manipulation of complex data and use of statistical software packages
  • learn to interpret and report the result from data analyses
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Why this course?

Our MSc in Applied Statistics is a conversion course, offering the opportunity to develop skills in statistics and data analysis even if you have never studies statistics before. You will be supported by members of staff who work directly with industry to develop skills which are relevant to current areas of research including population health and medicine, animal and plant health, finance and business.

Students will gain skills in:

  • problem solving
  • analysis and manipulation of complex data
  • the use of statistical software for data analysis and reporting
  • effective communication of statistics

Programme skillset

On the MSc Applied Statistics programme you'll have the opportunity to acquire:

  • an in-depth knowledge of modern statistical methods used to analyse and visualise real-life data sets, and the experience of how to apply these methods in a professional setting
  • skills in using statistical software packages used in government, industry and commerce
  • the ability to interpret the output from statistical tests and data analyses, and communicate your findings to a variety of audiences including health professionals, scientists, government officials, managers and stakeholders who may have an interest in the problem
  • problem solving and high numeracy skills widely sought after in the commercial sector
  • practical experience of statistical consultancy and how to interact with professionals who require statistical analyses of their data

Statistical information on laptop screen

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What you’ll study

In addition to compulsory modules, there are a range of elective modules, meaning you can tailor the course in line with your career interests.

Semester 1
In semester 1, modules focus on the foundations of statistics. You’ll learn about probability, and basic statistical analysis, as well as developing skills in programming in the statistical programming language R.

Semester 2
In semester 2, modules will build on concepts from year 1. These will focus on methods of analysis that can be applied to specific areas, such as medical trials, risk analysis, and finance.

Semester 3
In semester 3, you'll undertake a research project in which you'll work on a real-life data set, putting the theoretical skills you have learned into practice.

Learning & teaching

Classes are delivered by a number of teaching methods:

  • lectures (using a variety of media including electronic presentations and computer demonstrations)
  • tutorials
  • computer laboratories
  • coursework
  • projects

Teaching is student-focused, with students encouraged to take responsibility for their own learning and development. Classes are supported by web-based materials.


The form of assessment varies from class to class. For most classes the assessment involves both coursework and examinations.

  • The assessment will take ask you to demonstrate your statistical knowledge and skills to analyse real world data and interpret the results in the context of the research question
  • Projects will involve writing code, interpreting statistical outputs, and producing a report, or presentation outlining the findings from your analysis
  • Group work may be undertaken in some classes


The Department of Mathematics & Statistics has teaching rooms which provide you with access to modern teaching equipment and University computing laboratories, with all necessary software available.

You'll also have access to a common room facility which gives you a modern and flexible area for individual and group study work and is also a relaxing social space.

The Department of Mathematics & Statistics

At the heart of the Department of Mathematics & Statistics is the University’s aim of developing useful learning. We're an applied department with many links to industry and government. Most of the academic staff teaching on this course hold joint-appointments with, or are funded by, other organisations, including APHA, Public Health and Intelligence (Health Protection Scotland), NHS Greater Glasgow and Clyde and the Marine Alliance for Science and Technology Scotland (MASTS). We bridge the gap between academia and real-life. Our research has societal impact.

Teaching staff

Staff memberResearch Expertise
Dr Neil Banas An oceanographer and mathematical ecologist, with a background in physical oceanography. Current research investigating how climate change affects marine ecosystems and the role of biological complexity (diversity, adaptability, behaviour, life history) in large-scale patterns in the ocean.
Dr Bingzhang Chen Current research is on how biodiversity affects marine ecosystem functioning such as primary production and biological carbon pump, for which the primary producers particularly phytoplankton play the pivotal role.
Dr Alison Gray Research interests cover pattern recognition and machine learning, image analysis, applied epidemiology, SDE models for epidemics, and applications of statistics for honey bee research.
Prof David Greenhalgh Research interests include mathematical and statistical techniques applied to biological problems, in particular mathematical and statistical modelling in epidemiology.

Dr Kim Kavanagh

Statistical expertise in the analysis and modelling of large observational health datasets with research interest in the fields of public health epidemiology, pharmacoepidemiology and digital health.
Dr Louise Kelly Part-time Senior Risk Analyst Animal and Plant Health Agency (APHA) with research interests in veterinary and public health risk assessment and mathematical modelling projects relating to e.g. bovine tuberculosis, bovine brucellosis, foot and mouth disease, bluetongue, campylobacter, salmonella and rabies.
Prof Chris Robertson Professor of Public Health Epidemiology in the Department of Mathematics and Statistics, and Head of Statistics at Public Health Scotland. Main research interest is in statistical modelling of infectious diseases and in epidemiological studies.
Dr David Young Part-time Senior Consultant Statistician for NHS Scotland with research interests in design, conduct and analysis of medical research studies.

Dr Kate Pyper

Research focuses on applications of statistics to health and medical datasets, with a particular interest in spatio-temporal modelling. Involved in consultancy involving data visualisation and data dashboard development.

Dr Ainsley Miller

Teaching Associate with interests in mathematics and statistical pedagogy, in particular easing the transition from school to university and understanding the mental health struggles of students. Member of the core team of the Scottish Qualification Authority's Higher Applications of Mathematics course. Qualified Mental Health First Aider and Sexual Assault First Responder who runs a support service for all mathematics and statistics students.

Ryan Stewart

Teaching Associate with interest in statistical pedagogical research. Statistical expertise in the linkage and analysis of large administrative datasets in the field of public health epidemiology and policy. Member of Higher Education Academy.

Andrew Browne

Previous research experience includes analysis of data from clinical trials, observational studies, and systematic reviews. Teaching and pedagogical interests focus on the teaching of statistics to those from other disciplines.

Dr Louise Kelly
The training is fast-paced, bringing students up to speed with the necessary practical skills in a very short time period. This means that our graduates are very attractive to government and industry.
Dr Louise Kelly
Postgraduate Taught Director, Department of Mathematics and Statistics
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Course content

Semester 1 modules are compulsory. In Semester 2 you are required to take 60 credits of optional modules. With the approval of the Course Director, students may substitute other appropriate modules offered by the University for one or more of the optional modules listed below.

Foundations of Probability & Statistics (20 credits)

The course and thus this introductory module is aimed at graduates who've not previously studied statistics at university level. The module will provide the foundation elements of probability and statistics that are required for the more advanced classes studied later on.

This will include:

  • an introduction to probability distributions
  • introductory hypothesis testing
  • non-parametric hypothesis testing
  • linear regression
  • introductory power and sample size calculations
Data Analytics in R (20 credits)

This module will introduce the R computing environment and enable you to import data and perform statistical tests. The module will then focus on the understanding of the least squares multiple regression model, general linear model, transformations and variable selection procedures.

You can expect to cover concepts such as:

  • use of functions and packages in R
  • use of the tidyverse for data manipulation
  • data visualisation using both base R and ggplot2
  • multiple linear regression
  • using variable selection techniques to cope with large data sets
  • more general model comparison
Experimental Design (10 credits)

This module provides students with the fundamental principles of statistical modelling through experimental design. The statistical models used in the analysis of balanced experimental designs are derived and used in the analysis of data sets.

You will cover topics such as:

  • completely randomised design
  • randomised block experiments
  • factorial experiments and interactions
  • nested designs and repeated measures designs
Multivariate Analysis (10 credits)

This module aims to provide students with a range of applied statistical techniques that can be used in professional life to analyse multivariate data. Both statistical and machine learning approaches are included.

You will cover topics such as:

  • graphical methods for investigating multivariate data
  • logistic regression and discrimination
  • linear and quadratic discriminant analysis
  • non-parametric classification
  • hierarchical and non-hierarchical clustering
  • principal component analysis.
Medical Statistics (20 credits)

This module will cover the fundamental statistical methods necessary for the application of classical statistical methods to data collected for health care research. There will be an emphasis on the use of real data and the interpretation of statistical analyses in the context of the research hypothesis under investigation.

Topics covered will include:

  • survival analysis
  • analysing categorical data using hypothesis tests
  • experimental Design and sampling
  • clinical measurement
Quantitative Risk Analysis (10 credits)

This module will cover the theory of assessing risks under uncertainty. It will focus on the practical assessment of risk using simulation methods such as Monte Carlo simulation. You'll develop skills in communicating risk to risk managers as well as formulating practical risk questions that can influence policy decisions.

You can expect to learn about:

  • uncertainty and variability
  • bootstrapping
  • Monte Carlo Simulation
  • selecting appropriate probability distributions based on given scenarios
Survey Design & Analysis (10 credits)

Surveys are an important way of collecting data. This module will introduce you to the methods that are commonly used in health care to design questionnaires and analyse data resulting from these questionnaires.

You will consider:

  • how to design appropriate survey questions
  • a variety of sampling methods
  • analysing data for different sampling methods
Bayesian Spatial Statistics (20 credits)

This module will introduce you to Bayesian statistics and the modern Bayesian methods that are used in a variety of applications. Like with other modules, the focus is on real-life data and using statistical software packages for analysis.
You will gain experience in working with the following:

  • visualising spatial data
  • geospatial data, including methods for prediction
  • bayesian modelling using software to implement Markov Chain Monte Carlo
  • areal unit modelling
Effective Statistical Consultancy

This module covers all aspects of statistical consultancy skills necessary for being a successful statistician working in any research environment. You'll work on real-life problems in small groups and have the opportunity to interact with healthcare researchers to formulate hypotheses.

This module will cover:

  • how to engage with professionals working in business, industry and the public sector
  • how to apply their statistical knowledge in different situations
  • how to effectively communicate statistical results to non-statisticians
Financial Econometrics (10 credits)

You'll be exposed to a number of diverse topics in econometrics that can be used to model real financial data, with an emphasis on the analysis of financial time series. The statistical software R is introduced for financial modelling.

Topics covered will include:

  • basic statistics in finance
  • Time Series modelling
  • financial volatility modelling
  • forecasting
Financial Stochastic Processes (10 credits)

This module aims to expose you to a number of diverse topics in stochastic processes that can be used to model real systems, with an emphasis on the valuation of financial derivatives. In additional to theoretical analysis, appropriate computational algorithms using R are introduced.

Topics covered will include:

  • how stochastic models arise
  • financial options
  • the Black-Scholes equation
  • simulation of financial mathematical models
Business Analytics (20 credits)

Every two days, we generate as much data as the data generated in all human history up to 2003. From online data on every click of the mouse on the internet through the huge upsurge in manufacturing companies’ use of sensors to sports organisations collecting in-game data. With these increased quantities of data comes an increased need for tools to make sense of the main messages coming from these data.

The module will build on the fundamental multivariate statistics by developing both visualisation and advanced analysis techniques relevant in the area of big data. The focus will be on application and interpretation of techniques and there will be an investigation of what makes good data. The module will develop both new theoretical knowledge in the form of analytics techniques as well as new software skills in relevant analytics software.

Research project (60 credits)

You'll undertake a research project in which you'll work on a real-life data set, putting the theoretical skills you have learned into practice.

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Entry requirements

Academic requirements/experience

Minimum second-class (2:2) Honours degree or overseas equivalent.

Mathematical training to A Level or equivalent standard.

Prospective students with relevant experience or appropriate professional qualifications are also welcome to apply.

For Australia and Canada, normal degrees in relevant disciplines are accepted.

Mathematical knowledge

Applicants are required to have some prior mathematical knowledge, eg A Level or equivalent in:

  • calculus
  • linear algebra
  • differential equations

If you have any questions, email (

English language requirements

You must have an English language minimum score of IELTS 6.0 (with no component below 5.5).

We offer comprehensive English language courses for students whose IELTS scores are below 6.0. Please see ELTD for full details.

As a university, we now accept many more English language tests in addition to IELTS for overseas applicants, for example, TOEFL and PTE Cambridge. View the full list of accepted English language tests here.

Pre-Masters preparation course

The Pre-Masters Programme is a preparation course held at the University of Strathclyde International Study Centre, for international students (non EU/UK) who do not meet the academic entry requirements for a Masters degree at University of Strathclyde. The Pre-Masters programme provides progression to a number of degree options.

Upon successful completion, you'll be able to progress to this degree course at the University of Strathclyde.

International students

We've a thriving international community with students coming here to study from over 100 countries across the world. Find out all you need to know about studying in Glasgow at Strathclyde and hear from students about their experiences.

Visit our international students' section

Map of the world.

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Fees & funding

All fees quoted are for full-time courses and per academic year unless stated otherwise.

Fees may be subject to updates to maintain accuracy. Tuition fees will be notified in your offer letter.

All fees are in £ sterling, unless otherwise stated, and may be subject to revision.

Annual revision of fees

Students on programmes of study of more than one year should be aware that tuition fees are revised annually and may increase in subsequent years of study. Annual increases will generally reflect UK inflation rates and increases to programme delivery costs.

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England, Wales & Northern Ireland




Available scholarships

Take a look at our scholarships search for funding opportunities.

Additional costs

If you are an international student, you may have associated visa and immigration costs. Please see student visa guidance for more information. 

Please note: the fees shown are annual and may be subject to an increase each year. Find out more about fees.

How can I fund my course?

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Scottish postgraduate students

Scottish postgraduate students may be able to apply for support from the Student Awards Agency Scotland (SAAS). The support is in the form of a tuition fee loan and for eligible students, a living cost loan. Find out more about the support and how to apply.

Don’t forget to check our scholarship search for more help with fees and funding.

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Students coming from England

Students ordinarily resident in England may be to apply for postgraduate support from Student Finance England. The support is a loan of up to £10,280 which can be used for both tuition fees and living costs. Find out more about the support and how to apply.

Don’t forget to check our scholarship search for more help with fees and funding.

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Students coming from Wales

Students ordinarily resident in Wales may be to apply for postgraduate support from Student Finance Wales. The support is a loan of up to £10,280 which can be used for both tuition fees and living costs. Find out more about the support and how to apply.

Don’t forget to check our scholarship search for more help with fees and funding.

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Students coming from Northern Ireland

Postgraduate students who are ordinarily resident in Northern Ireland may be able to apply for support from Student Finance Northern Ireland. The support is a tuition fee loan of up to £5,500. Find out more about the support and how to apply.

Don’t forget to check our scholarship search for more help with fees and funding.

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International students

We've a large range of scholarships available to help you fund your studies. Check our scholarship search for more help with fees and funding.

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We work closely with the University's Careers Service. They offer advice and guidance on career planning and looking for and applying for jobs. In addition they administer and publicise graduate and work experience opportunities.

There are many exciting career opportunities for graduates in applied statistics. The practical, real-life skills that you'll gain means you'll be much in demand in international organisations. A report by the Association of the British Pharmaceutical Industry identified statistics and data mining as “two key areas in which a 'skills gap' is threatening the UK's biopharmaceutical industry.”

Graduates from the MSc Applied Statistics programme have gone on to be employed in a number of different sectors such as:  

  • Clinical Trials Statistician at Usher Institute
  • Data Analyst at Bending Spoons
  • Associate Statistician at Thermo Fisher Scientific
  • Biostatistician at Optical Express
  • Statistician at Phastar (x7)
  • Statistician at Quotient Sciences (x3)
  • Information Analyst at NHS Scotland
  • Statistician at Scottish Government (x4)
  • Statistician at Abbots Diabetes Care
  • Medical Statistician at University of Oxford
  • Credit Risk Analyst at Clydesdale Bank
  • Statistical Analyst at Medpace
  • Data Scientist at Scottish Water
  • PhD studentship in social sciences
Glenn McCreadie, MSc Applied Statistics graduate
The course offered a wide variety of optional courses to choose from which would help to tailor my experience helping to become more specialised and prepare myself for many fields of industry.
Glenn McCreadie

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Our campus is based right in the very heart of Glasgow. We're in the city centre, next to the Merchant City, both of which are great locations for sightseeing, shopping and socialising alongside your studies.

Life in Glasgow

Gallery of Modern Art, Royal Exchange Square.

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Apply for on-campus delivery below or apply to online version of MSc Applied Statistics

Start date: Sep 2023

Applied Statistics (on campus)

Start date: Sep 2023