MSc Applied Statistics in Health Sciences (online)
ApplyKey facts
- Start date: September or January
- Accreditation: Royal Statistical Society: MSc graduates may qualify for GradStat status
- Study mode and duration: online across 24 or 36 months, part-time. Standalone modules can also be taken for CPD purposes or working towards an MSc over a maximum of 60 months.
Study with us
- a conversion course, designed 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
Why this course?
Our course is run by academics who work in the health sector as well as in higher education. Statisticians from the Animal and Plant Health Agency (APHA), an Executive Agency of the Department for Environment, Food & Rural Affairs (Defra) as well as those who have extensive experience in working with the National Health Service (NHS) in Scotland, will provide lectures based around real-life problems and data from the health sciences.
The course is entirely delivered online. The course is ideally suited to those working full-time or with other commitments. You can study and complete the modules when it’s most convenient for you – you don’t need to be online at specific times.
Although the programme is focused on health, the skills set provided will also equip you with the necessary training to work as an applied statistician in other areas such as insurance, finance and commerce.
You can also study the MSc in Applied Statistics in Health Sciences full-time on campus.
Programme skills set
On the online Applied Statistics in Health Sciences MSc programme you'll have the opportunity to acquire:
- 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
Accreditation
On successful completion of the MSc, you may be eligible for GradStat status. This may be awarded by submitting a transcript to the Royal Statistical Society as part of the evidence of meeting RSS GradStat criteria.
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. These include APHA, Public Health and Intelligence (Health Protection Scotland), Greater Glasgow and Clyde Health Board, and the Marine Alliance for Science and Technology Scotland. We bridge the gap between academia and real-life. Our research has societal impact.
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, Senior Lecturer
Wanting to balance work with study, I was looking for an MSc Statistics program that could be applied to my daily work as well as one that extended my knowledge and improve my employability. I chose the online MSc Applied Statistics at the University of Strathclyde where the course modules cover a good range of solid and practical topics, while giving a timeframe which works well for full-time working professionals. Although the program is online, I did not feel there was any gap with the lecturers as they are very passionate and engaged. It is a fantastic programme, which I strongly recommend to any individual who is interested in statistics as a key focus of their career path.
Thi Nguyen, MSc Applied Statistics with Finance (online) student
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Course content
- Throughout your studies, you will take 80 credits of compulsory taught classes, 40 credits of elective taught classes, and in your final year you'll also undertake your MSc Project (60 credits)
- September start programme terms are as follows:
- Term 1 September to December
- Term 2 January to April
- Term 3 April to July
- January start programme terms are as follows:
- Term 1 January to April
- Term 2 April to July
- Term 3 September to December
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
Statistical Modelling & Analysis
20 credits
This module will cover the fundamental statistical methods necessary for the design and analysis of scientific experiments. 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.
You will cover topics such as:
- analysing designed experiments such as randomised block, factorial, nested and repeated measures designs
- classification techniques such as logistic regression, nearest neighbours and discriminant analysis
- clustering techniques
- dimension reduction using 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
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.
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
I loved the broad range of topics covered as it gave me an overview of the application of statistics in health science as well as the balance between theory and practical skills.
Andrew, MSc Applied Statistics in Health Sciences (online)
Teaching staff
The following staff are all involved in the teaching and research project supervision (project availability may vary year-to-year).
| Staff Member | Research Expertise |
|---|---|
| Dr Bingzhang Chen | An ecologist focusing on marine plankton and has over 15 years of experience in employing various statistical techniques such as generalised linear and nonlinear models, Bayesian inference, and machine learning to analyse marine plankton data. His main research interests are how our ocean ecosystem will respond to warming. |
| Dr Tunde Csoban | Teaching Associate with research interests in women’s health, mental health, equity, diversity, and inclusion. Expertise in predictive modelling and machine learning, with a focus on using R Shiny to deploy predictive models. Strong interest in online learning and improving accessibility in education. Qualified Mental Health First Aider. |
| Dr Alison Gray | Research interests centre on applications of statistics in honeybee research, including conducting an annual survey of beekeepers in Scotland, as well as statistical and machine learning applied to environmental data. Previous research has also included modelling in epidemiology and image analysis projects. |
| Dr Helen He | Lecturer in Medical Statistics, and a Real-World Evidence (RWE) pharmacoepidemiologist. Epidemiological study designs and statistical analysis/modelling are applied using routinely collected large observational health data to understand the use/safety of medicines/vaccines/medical devices, as well as disease epidemiology. |
| Dr David Hodge | Teaching Associate with particular interests in probability and applications of probability and statistics to decision making under uncertainty. Senior Fellow of the Higher Education Academy and Royal Statistical Society Statistical Ambassador for media engagement around probability and statistics. |
| 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 | Senior Teaching Fellow with a general interest in quantitative risk assessment and epidemiology. Recent research focused on statistical modelling of education outcomes and their association with equality, diversity and inclusion characteristics. Previously worked as a Senior Risk Analyst for the Veterinary and Plant Health Agency, Department for Food and Rural Affairs, UK Government. Consultancy for the World Health Organisation and Office for Animal Health on quantitative risk assessment. |
| Prof Adam Kleczkowski | Works on modelling of disease systems at the interface of epidemiology, socio-economics and policy, from plants and trees (agricultural and forest hosts), through animal (Bovine Viral Diarrhea) to human diseases (measles, Norovirus, and pandemic influenza and COVID-19). |
| Dr Ainsley Miller | Teaching Fellow with a focus on mathematics and statistical pedagogy particularly in supporting students' transition to university. Core member of the SQA’s Higher Applications of Mathematics course. Qualified Mental Health First Aider and Sexual Assault First Responder, offering dedicated support to mathematics and statistics students. Fellow of the Higher Education Academy. |
| Dr Jiazhu Pan | Main research interests include Time Series Analysis and Econometrics with applications in modelling complex spatio-temporal data from finance, environmental science and health science. |
| Prof Chris Robertson | Professor of Public Health Epidemiology in the Department of Mathematics & Statistics, and Statistical Advisor at Public Health Scotland. Main research interest is in statistical modelling of infectious diseases and in the design of epidemiological studies and disease surveillance systems. He has considerable expertise in the analysis of administrative electronic health records. |
| Dr Ryan Stewart | Teaching Associate with interest in oral health and statistical pedagogical research. Statistical expertise in the linkage and analysis of large administrative datasets in the field of public health epidemiology and policy. Fellow of Higher Education Academy. |
| Dr Florence Tydeman | Research Associate in Statistics and Knowledge Exchange, with a joint appointment between King’s College London and the University of Strathclyde. Main research focuses on public health epidemiology, particularly in women’s and children’s health, utilising statistical models to analyse large observational health datasets, spanning academic and clinical research projects. |
| Dr David Young | Part-time Senior Consultant Statistician for NHS Scotland with research interests in the design, conduct and analysis of medical research studies. |
| Connor Watret | Teaching Associate with an interest in disease modelling in UK forests. My PhD research has been exploring the impact Ash Dieback has had on the UK's Ash population. |
| Dr Suzy Whoriskey | Director of Knowledge Exchange in Mathematics & Statistics with research interests in applied statistics, methodology development and high-dimensional data. Experience working in health statistics, agriculture applications, and statistical genetics. Obtained the Universal Design in Teaching & Learning Digital Badge during completion of UCD’s Professional Certificate in Teaching & Learning. Oversees mathematical collaborations with businesses, industry, governmental bodies and charities. |
| Dr Yue Wu | PhD in Stochastic Analysis from Loughborough University, with extensive student supervision experience at the University of Oxford, UCL, University of Edinburgh, and Loughborough University. Research interests focus on leveraging mathematical tools in machine learning and AI to capture and analyze the dynamic profiles of longitudinal and complex multimodal data. These methods hold potential for addressing critical challenges in fields such as healthcare, AI security, and environmental science. |
Learning & teaching
Classes are delivered using the MyPlace online teaching environment hosted by the University of Strathclyde.
You’ll learn through video lectures, interactive sessions, independent reading of articles and texts and discussion forums.
On average, you'll study five hours of online material per module per week, plus additional self-study. You’ll also have regular assistance from dedicated tutors who'll interact and communicate with you through online forums and email.
You'll be part of a community of students working collaboratively to share and enhance learning.
Assessment
All assessment will be undertaken online. The assessment will take the form of large-scale projects where you’ll be asked to demonstrate your knowledge on a real-world data set. 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.
Entry requirements
| Academic requirements/experience | Minimum second-class (2:2) Honours degree or international 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, for example 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. |
Fees & funding
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.
| 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. |
|---|---|
| Tuition fees |
For those intending to study stand-alone modules, the cost will be £847 per 10-credit module payable on registration. |
| Available scholarships | Scholarships of £1,800 are available to new students joining for September entry of one of our online programmes in the 2024/2025 academic year. We have a Faculty of Science scholarship for online students available to help fund your studies. Find out more. Take a look at our scholarships search for funding opportunities. |
| Additional costs | International students may have associated visa and immigration costs. Please see student visa guidance for more information. |
Fees & funding
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.
| 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. |
|---|---|
| Tuition fees |
For those intending to study stand-alone modules, the cost will be £847 per 10-credit module payable on registration. |
| Available scholarships | Scholarships of £1,800 are available to new students joining for September entry of one of our online programmes in the 2024/2025 academic year. We have a Faculty of Science scholarship for online students available to help fund your studies. Find out more. Take a look at our scholarships search for funding opportunities. |
| Additional costs | International students may have associated visa and immigration costs. Please see student visa guidance for more information. |
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
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.”
Typical employers of statisticians and data analysts include:
- government
- health services
- pharmaceutical companies
- human, animal, plant and environmental research institutes
- insurance companies
- banks
- internet information providers such as Google
- retailers
Typical graduate roles
Typical job roles of recent graduates include:
- statistician
- data analyst
- statistical programmer
- data scientist
I chose Strathclyde for the attractive course content and distance learning option. Everything covered was grounded in practical examples and case studies felt realistic and believable. The lecturers would typically have a wealth of professional experience and could comment on the theory as well as what you would do 'in the real world'. The course was well organised with material/assessments released in a manageable way. Students felt listened to and, where the schedule could be adjusted to help with time management, it was. I felt supported and enabled to do well by lecturers who were always willing to go the extra mile to help.
Sam, MSc Applied Statistics in Health Sciences (online)
Apply
Modules can be taken either stand-alone for CPD purposes or as part of a programme of study working towards a MSc award over a maximum of 60 months. If interested in this mode of study, please email science-masters@strath.ac.uk for further information on how to apply.
Apply for online delivery below, or apply for MSc in Applied Statistics in Health Sciences full-time on campus.
Start date: Jan 2025
Applied Statistics in Health Sciences (2 year online) - January
Start date: Jan 2025
Applied Statistics in Health Sciences (3 year online) - January
Start date: Sep 2025
Applied Statistics in Health Sciences (3 year online) - September
Start date: Sep 2025
Applied Statistics in Health Sciences (2 year online) - September
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