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MSc Applied Statistics in Health Sciences (online)

Apply now for January 2023 entry
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Key facts

  • Start date: September or January
  • Accreditation: on successful completion of the MSc, you may be eligible for GradStat status
  • Study mode and duration: 36 months part-time, online or modules can be taken stand-alone online over a maximum 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
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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.

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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.

Dr Louise Kelly, senior lecturer in MSc Applied Statistics in Health Sciences

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

Find out more about the course from Louise
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Course content

Year 1

In Year 1 you’ll study three modules, equipping you with fundamental probability and data analysis skills. Our year 1 modules focus on the foundations of statistics. You’ll learn about probability, and basic statistical analysis, as well as developing skills in programming in the statistical programming language R.

Year 2

In Year 2 there are five modules, each focusing on a different applied element of being a statistician. Year 2, modules will build on concepts from year 1. These will focus on methods of analysis that can be applied to specific areas, such as medical trials, risk analysis, and finance.

Year 3

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

Year 1

September - 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

January - April

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

April - July

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

Year 2

September - December

Quantitative Risk Analysis (10 credits)

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

You can expect to learn about:

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

Surveys are an important way of collecting data. This 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

January - April

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

April - July

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
Bayesian Spatial Statistics (20 credits)

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

You will gain experience in working with the following:

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

Year 3

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.

Year 1

January - April

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

April - July

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

September – December

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

Year 2

September - December

Quantitative Risk Analysis (10 credits)

This class 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.

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

January - April

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

April - July

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
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

Year 3

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.

The Queen's Anniversary Prizes for Higher and Further Education 2019 and 2021.
The Queen's Anniversary Prizes for Higher and Further Education 2019 and 2021.
The Times / The Sunday Times Good University Guide 2021. University of the Year shortlisted.
The Times / The Sunday Times Good University Guide 2021. University of the Year shortlisted.
The Times & Sunday Times Good University Guide 2020 - Scottish University of the Year.
The Times & Sunday Times Good University Guide 2020 - Scottish University of the Year.

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 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.

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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:

  • calculus
  • linear algebra
  • differential equations

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.

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Fees & funding

All fees quoted are per academic year unless stated otherwise.

The MSc in Applied Statistics in Health Sciences (online) consists of 180 credits studied over three years. You'll study 60 credits annually.

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Tuition fees

£3,850 (60 credits)

Stand-alone modules

£1,283 per 20 credit module*

*payable on registration.

Available scholarships

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.

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

How can I fund my course?

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Scottish postgraduate students

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

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

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Students coming from England

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

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

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Students coming from Wales

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

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

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Students coming from Northern Ireland

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

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

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International students

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

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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

Abstract image of doctor looking at tablet, graphs overlayed.

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)

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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.

Please note, there is no deadline for submitting applications.

Start date: Jan 2023

Applied Statistics in Health Sciences (online) - January intake

MSc
part-time
Start date: Jan 2023

Start date: Sep 2023

Applied Statistics in Health Sciences (online)

MSc
part-time
Start date: Sep 2023

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Contact us

PGT Admissions Team

Telephone: +44 (0)141 574 5147

Email: science-masters@strath.ac.uk