MSc Applied Statistics (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 online MSc in Applied Statistics is a conversion course, offering the opportunity to develop skills in statistics and data analysis even if you have never studied statistics before.
You'll be supported through this 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. Modules are delivered entirely online, giving students the flexibility to study at times that suit them. You'll 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 online Applied Statistics 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
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. |
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 60 credits of compulsory taught classes, 60 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'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 modules 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
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 50 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 module will introduce you to the methods 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
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
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
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 |
|
|---|---|
| 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. |
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. |
| Additional costs | International students may have associated visa and immigration costs. Please see student visa guidance for more information. |
| Available scholarships | Scholarships of £1,800 are available to new students joining for September entry of one of our online programmes in the 2025/26 academic year. Take a look at our scholarships search for funding opportunities. |
Careers
The online MSc in Applied Statistics will provide graduates with skills in the statistical analysis of data from a wide range of disciplines. These skills are required by many employers in sectors such as:
- investment companies
- financial institutions
- pharmaceutical industry
- medical research
- government organisations
- retailers
- internet information providers
Typical graduate roles
Typical job roles of recent graduates include:
- statistician
- data analyst
- statistical programmer
- data scientist
Any questions?
Below you'll find answers to some commonly asked questions about our Mathematics and Statistics online program, covering topics such as study commitment, degree certification, online resources, access to campus facilities and program flexibility.
You can study when suits you – although we would expect you to spend around 7-10 hours each week on the course (per module).
Our online courses are perfect for those with full-time jobs or family responsibilities. You can learn at a pace that works for you and fit your studies around your schedule, making it easier to balance your personal and professional life.
No, your degree certificate will look the same as the degree certificates received by those who have completed their study on campus.
Yes. Our online students are invited to campus for graduation ceremonies along with our on-campus students although this is not compulsory as we can post your certificate to your home address.
Yes, these awards are typically posted before the end of the calendar year. For noting, students who have been awarded a Postgraduate Diploma can receive their award at one of the winter graduation ceremonies.
Yes, online learning students can access the library, student union, leisure centre and any other on-campus facility which is available to our on-campus students.
The Department of Mathematics and Statistics have created a flexible pre-sessional mathematics module to prepare for undertaking this MSc.
Pre-Sessional Mathematics is a flexible online short course that takes you through some of the mathematics which underpins many statistical ideologies such as probability and statistical distributions.
The course is free for all MSc offer holders in the Department of Mathematics and Statistics.
The classes are taught through the University’s portal ‘MyPlace’ and would be videos that you can watch and complete in your own time, and materials that you can work through at your own pace. There is no requirement to be on campus.
Through MyPlace you can have online chats/forums with other students in the group. Students may also choose to communicate via chat groups using social media platforms.
You will be able to get in touch with your programme director via MyPlace or by email to message them with any questions relating to the programme content.
All modules are continually assessed via projects which are released during each term and are due after the taught component of each module is finished.
To obtain GradStat status, you would need to apply through the RSS to get your specific curriculum approved – and match the modules that you have undertaken to the key competencies that they have. This shouldn’t be a problem, but because the curriculum is not fixed for this program each individual curriculum must be approved separately.
Yes, if you decide that you would like to switch to the 3-year programme we can accommodate this for you. Your course director would be able to discuss what this would mean for your studies.
Unfortunately, due to logistical reasons and when the modules run, it is not possible to switch to the 3-year programme after you have started the 2-year programme. You can, however, switch before you begin your studies.
We would recommend that you start with Foundations of Probability & Statistics and Data Analytics in R. However, depending on your experience you may be able to complete another module first.
All learning is completed online so you would be expected to be familiar and comfortable using a computer/laptop. All software required is provided through the University and is free to use. The main statistical packages used are Open Source and free to download and use once your studies have finished.
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 to on-campus version of MSc Applied Statistics.
Start date: Sep 2025
Applied Statistics (3 year online) - September
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Applied Statistics (2 year online) - September
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Applied Statistics (2 year online) - January
Start date: Jan 2026
Applied Statistics (3 year online) - January
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