Program Overview

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MSc Applied Statistics (online)

The 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.

Key Facts

• Start date: September or January
• Accreditation: Royal Statistical Society: MSc graduates may qualify for GradStat status
• Study mode and duration: online over 2 or 3 years, part-time. Standalone modules can also be taken for CPD purposes or working towards an MSc over a maximum of 5 years.

Study with Us

The online MSc in Applied Statistics is a conversion course, designed for those with a background in a broad range of disciplines. You will gain skills in problem-solving, the analysis and manipulation of complex data, and use of statistical software packages. You will learn to interpret and report the results 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.

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).

• 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.
• Dr Tunde Csoban: Teaching Associate with research interests in women’s health, mental health, equity, diversity, and inclusion.
• 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.
• Dr Helen He: Lecturer in Medical Statistics, and a Real-World Evidence (RWE) pharmacoepidemiologist.
• Dr David Hodge: Teaching Associate with particular interests in probability and applications of probability and statistics to decision making under uncertainty.
• 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.
• 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.
• 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.
• Dr Ryan Stewart: Teaching Associate with interest in oral health and statistical pedagogical research.
• Dr Florence Tydeman: Research Associate in Statistics and Knowledge Exchange, with a joint appointment between King’s College London and the University of Strathclyde.
• 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.
• Dr Suzy Whoriskey: Director of Knowledge Exchange in Mathematics & Statistics with research interests in applied statistics, methodology development and high-dimensional data.
• 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.

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).

Compulsory Classes

• Foundations of Probability & Statistics: The course and thus this introductory module is aimed at graduates who've not previously studied statistics at university level.
• Data Analytics in R: This module will introduce the R computing environment and enable you to import data and perform statistical tests.
• Statistical Modelling & Analysis: This module will cover the fundamental statistical methods necessary for the design and analysis of scientific experiments.

Elective Classes

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: This module will cover the theory of assessing risks under uncertainty.
• Bayesian Spatial Statistics: This module will introduce you to Bayesian statistics and the modern Bayesian methods that are used in a variety of applications.
• Survey Design & Analysis: Surveys are an important way of collecting data.
• Effective Statistical Consultancy: This module covers all aspects of statistical consultancy skills necessary for being a successful statistician working in any research or customer environment.
• Medical Statistics: This module will cover the fundamental statistical methods necessary for the application of classical statistical methods to data collected for health care research.
• Financial Econometrics: 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.
• Financial Stochastic Processes: 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.
• Data dashboards with RShiny: This module will develop your skills in data presentation and statistical communication.
• Big Data Tools & Techniques: 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.
• Big Data Fundamentals: 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.
• Machine Learning for Data Analytics: This module will provide you with a sound understanding of the principles of Machine Learning and a range of popular approaches.

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.

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

• 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
• English language minimum score of IELTS 6.0 (with no component below 5.5)

Fees & Funding

• 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.
• 2025/26: £5,600 (3-year programme, price per year), £8,400 (2-year programme, price per year)
• Additional costs: International students may have associated visa and immigration costs.
• Available scholarships: Scholarships of £1,800 are available to new students joining for January entry of one of our online programmes in the 2025/26 academic year.

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 are 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.

How many study hours a week am I required to commit to?

You can study when suits you – although we would expect you to spend around 7-10 hours each week on the course (per module).

Am I able to study and work at the same time?

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.

Will my degree certificate show that my degree was completed online?

No, your degree certificate will look the same as the degree certificates received by those who have completed their study on campus.

Can I graduate on campus at the University of Strathclyde?

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.

Will I be eligible for a certificate if I exit with a PGCert or PGDip?

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.

As an online learning student, will I have access to the on-campus facilities of the University of Strathclyde?

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.

I need to brush up on my mathematical knowledge, is there any preparation material available to me?

The Department of Mathematics and Statistics have created a flexible pre-sessional mathematics module to prepare for undertaking this MSc.

Is there a cost for undertaking the Pre-Sessional Mathematics course?

The course is free for all MSc offer holders in the Department of Mathematics and Statistics.

How are classes taught? Is there any requirement to attend lessons on campus?

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.

How would I network with other students during the online programmes?

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.

How can I contact my lecturer if I have any questions?

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.

How will I be assessed if the programme is entirely online?

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.

How can I obtain GradStat Status?

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.

If I am studying the 2-year programme, am I able to switch to the 3-year programme?

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.

Alternatively, if I am studying the 3-year programme, am I able to switch to the 2-year programme?

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.

I would like to enrol on the modular version of the programme, are there any mandatory modules I would be required to undertake and am I able to start a specific module at any time?

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.

Are there any technical requirements I should be aware of?

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.