MSc Applied Statistics in Health Sciences
ApplyKey facts
- Start date: September
- Accreditation: Royal Statistical Society: MSc graduates may qualify for GradStat status
- Study mode and duration: 12 months full-time (on campus)
Study with us
- designed for those with a background in a broad range of disciplines
- learn about probability and how to analyse data
- gain skills in problem-solving, big data and use of statistical software packages
Why this course?
Our MSc in Applied Statistics in Health Sciences is a conversion course, offering the opportunity to develop skills in statistics and data analysis even if you have never studied statistics before. You will be supported programme 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
Course video
Find out more about Applied Statistics in Health Sciences:
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What you'll study
The three modules covered in Semester 1 will equip you with fundamental probability and data analysis skills.
In Semester 2 there are four modules studying full-time on campus. Each focuses on a different applied element of being a statistician. The course concludes with a research project that will involve the analysis of a real-life data set.
Programme skills set
On the MSc Applied Statistics in Health Science 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 through the contacts with APHA and NHS staff, an understanding of what it's like to work as an applied statistician in practice including, for example, during disease outbreaks
Facilities
The Department of Mathematics & Statistics has teaching rooms which provide you with access to modern teaching equipment and computing laboratories that are state-of-the-art with all necessary software available. You'll also have a common room facility, a modern and flexible area which is used for individual and group study work, and 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), Greater Glasgow and Clyde Health Board and the Marine Alliance for Science and Technology Scotland (MASTS). We bridge the gap between academia and real-life. Our research has societal impact.
Guest lectures
Several modules will be taught by academics who also work for other organisations including government and health services.
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.
Senior Lecturer
Course content
- Throughout your studies, you will take 80 credits of compulsory taught classes, 40 credits of elective taught classes, and in the third (summer) term you'll also undertake your MSc Project (60 credits)
- Programmes terms are as follows:
- Semester 1 September to December
- Semester 2 January to April
- Semester 3 April to July
Compulsory classes
Foundations of Probability & Statistics
20 credits
The course and thus this introductory module is aimed at graduates who have 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.
Compulsory classes
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
Elective classes
Students are required to take at least 10 credits from List A and the remaining 30 credits can be from List A and/or List B modules.
List A
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
Bayesian Spatial Statistics
10 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
List B
Survey Design & Analysis
10 credits
Surveys are an important way of collecting data. This class will introduce you to the methods that are commonly used to design questionnaires and analyse data resulting from these questionnaires.
You’ll consider:
- how to design appropriate survey questions
- a variety of sampling methods
- analysing data for different sampling methods
Effective Statistical Consultancy
10 credits
This module covers all aspects of statistical consultancy skills necessary for being a successful statistician working in any research or customer environment. You will work on real-life problems in small groups and have the opportunity to interact with stakeholders researchers to formulate hypotheses.
This module will cover how to:
- engage with professionals working in business, industry and the public sector
- apply their statistical knowledge in different situations
- 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
Data dashboards with Rshiny
10 credits
This module will develop your skills in data presentation and statistical communication. You will learn to develop data dashboards, which are increasingly used to allow key stakeholders (and the public) to gain key insights into data via interactive visualisation.
Topics covered will include:
- Creating a data dashboard in RStudio
- User interface design with respect to accessibility
- Creating interactive data visualisations which reflect a specific aim
- Reactive programming in RStudio
- Static programming in R
Big Data Tools & Techniques
10 credits
This module will enhance your understanding of the challenges posed by the advent of Big Data and will introduce you to scalable solutions for data storage and usage.
You can expect to learn about:
- the design and implementation of cloud NoSQL systems
- addressing design trade-offs and their impact
- the Map-Reduce programming paradigm
Big Data Fundamentals
10 credits
This module will introduce the challenges of analysing big data with specific focus on the algorithms and techniques which are embodied in data analytics solutions.
At the end of the module, you'll understand:
- the fundamentals of Python for use in big data technologies
- how classical statistical techniques are applied in modern data analysis
- the limitations of various data analysis tools in a variety of contexts
Machine Learning for Data Analytics
20 credits
This module will provide you with a sound understanding of the principles of Machine Learning and a range of popular approaches. We'll provide a sound balance between theory and practical, hands-on applications using Python, so you should be familiar with programming in Python.
You can expect to learn about:
- machine learning: aims & fundamentals
- core machine learning algorithms
- understand when to apply which algorithm
- deep learning
- artificial neural networks
Statistical Machine Learning
10 credits
This module provides you with the basic theories of machine learning and how to construct a machine model for a real dataset using R. You will also understand the ethical issues regarding data processing and management.
On completion of this module, you will be able to:
- clean data using RStudio and the tidyverse
- understand missing data and the role it plays
- understand ethical issues regarding data processing and management
- carry out single-value imputation
- carry out multiple imputed chained equations in R
- understand and implement artificial neural networks
- understand and implement support vector machines
- understand and implement tree-based classification and regression techniques
- understand and implement ensemble methods
Compulsory classes
Research Project
60 credits
You undertake a research project in which you'll work on a real-life data set, putting the theoretical skills you have learned into practice. Working with APHA and the NHS on one of their policy-driven problems is possible.
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.
Assessment
The form of assessment varies from class to class. For most classes, the assessment involves both coursework and examinations.
- The assessment will 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
Please see Applied Statistics in Health Sciences (online) for online learning and teaching.
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:
If you have any questions, email (science-masters@strath.ac.uk). |
| 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-UK/Ireland) who do not meet the academic entry requirements for a Masters degree at University of Strathclyde.
Upon successful completion, you'll be able to progress to this degree course at the University of Strathclyde.
Please note: Previous Maths & English qualifications and your undergraduate degree must meet GTCS minimum entry requirements as well as the pre-Masters course and an interview will be conducted before an offer can be made.
I've become well acquainted with different statistical tools used in medical research, as well as other sciences - economics, political science and other social sciences - with programming in R, using different statistical software (such as Minitab or SPSS), academic writing and implementing independent research.
International students
We've a thriving international community with students coming here to study from over 140 countries across the world. Find out all you need to know about studying in Glasgow at Strathclyde and hear from students about their experiences.
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 (or studying standalone modules) should be aware that the majority of fees will increase annually. The University will take a range of factors into account, including, but not limited to, UK inflation, changes in delivery costs and changes in Scottish and/or UK Government funding. Changes in fees will be published on the University website in October each year for the following year of study and any annual increase will be capped at a maximum of 10% per year.
| Scotland | £11,900 |
|---|---|
| England, Wales & Northern Ireland | £11,900 |
| Republic of Ireland |
If you are an Irish citizen and have been ordinary resident in the Republic of Ireland for the three years prior to the relevant date, and will be coming to Scotland for Educational purposes only, you will meet the criteria of England, Wales & Northern Ireland fee status. For more information and advice on tuition fee status, you can visit the UKCISA - International student advice and guidance - Scotland: fee status webpage. Find out more about the University of Strathclyde's fee assessments process. |
| International | £25,500 |
| 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?
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.
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.
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.
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.
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.
Careers
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
The learning environment at Strathclyde is truly exceptional, providing an education that aligns perfectly with your aspirations and ambitions.
MSc Applied Statistics in Health Science student
I really like the methodology that the academic staff have chosen to develop the course. This combined with technological developments, makes the course valuable, necessary, and extremely interesting.
MSc Applied Statistics in Health Sciences student
Glasgow is Scotland's biggest & most cosmopolitan city
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.
Teaching staff
| Staff member | Research 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. |
| 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 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. |
Apply
Apply for on-campus delivery below or apply to online version of MSc Applied Statistics in Health Sciences.
Start date: Sep 2025
Applied Statistics in Health Sciences (on campus)
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