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MSc Applied Statistics with Data Science (online)

Key facts

  • Start date: September or January
  • Accreditation: On successful completion of the MSc, students 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, manipulation and interrogation of big data sets and use of programming languages commonly used in statistics and data science

  • learn to interpret and report the result from data analyses
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Why this course?

Our online MSc in Applied Statistics with Data Science 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 through this three-year part-time 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. Over the three years, our students gain skills in:

  • problem-solving
  • big data technologies
  • the use of statistical software for data analysis and reporting
  • Python and R programming for data analysis
  • cloud storage systems

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.

The course has been designed by academics who also work as statisticians in the public sector, who are experts in understanding real-life statistical problems, data, and relating theory to practice.

The skills set provided will also equip you with the necessary training to work as an applied statistician or data analyst/scientist in a broad range of areas including health, insurance, finance, and social sciences.

Programme skillset

On the online Applied Statistics with Data Science 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
  • skills in working with big data technologies including programming in Python and R
  • knowledge of cloud-based storage for large data sets

Abstract data concept.

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Teaching staff

Staff memberResearch expertise
Professor David Greenhalgh

Research interests include mathematical and statistical techniques applied to biological problems, in particular mathematical and statistical modelling in epidemiology. 

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

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. 

Professor Chris Robertson

Professor of Public Health Epidemiology in the Department of Mathematics & 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 the design, conduct and analysis of medical research studies.

What you'll study

In addition to compulsory modules, there are a range of elective modules, meaning you can tailor the course in line with your career interests.

Year 1

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, you’ll develop skills in storing and using big data with a particular focus on the Python programming language. Optional 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.

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.

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Course content

  • In Year 1 all classes are compulsory, this amounts to 60 credits
  • In Year 2 you're required to take 20 credits of compulsory classes and 40 credits of optional classes
  • In Year 3 you'll undertake your MSc Project
  • With the approval of the Course Director, you may substitute other appropriate classes offered by the University for one or more of the optional classes listed below

Year 1

September - 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 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 class provides students with the fundamental principles of statistical modelling through experimental design and multivariate analysis. The statistical models used in the analysis of balanced experimental designs are derived and used in the analysis of data sets. The fundamental principles of balance, replication, blocking, interactions, nesting and repeated measures are all covered. In the multivariate part of the class, the concepts of data reduction, clustering and classification are discussed.

Year 2

January - April

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
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

Electives

You're required to take 40 credits of elective classes:

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 will consider:

  • how to design appropriate survey questions
  • a variety of sampling methods
  • analysing data for different sampling methods

January - April

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
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
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

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 class is aimed at graduates who have not previously studied statistics at university level. The class will provide the foundation elements of probability and statistics that are required for the more advanced classes studied later on.

April - July

Data Analytics in R (20 credits)

This class will introduce the R computing environment and enable you to import data and perform statistical tests. The class will then focus on the understanding of the least squares multiple regression model, general linear model, transformations and variable selection procedures.

September - December

Statistical Modelling & Analysis (20 credits)

This class provides students with the fundamental principles of statistical modelling through experimental design and multivariate analysis. The statistical models used in the analysis of balanced experimental designs are derived and used in the analysis of data sets. The fundamental principles of balance, replication, blocking, interactions, nesting and repeated measures are all covered. In the multivariate part of the class, the concepts of data reduction, clustering and classification are discussed.

Year 2

January - April

Medical Statistics (20 credits)

This class 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. Software packages such as Minitab will be introduced.

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
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

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'll 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
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

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.

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.

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 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, such as 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

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

Please note, for courses that have a January 2023 start date, 2022/23 academic year fees will apply. For courses that have a September 2023 and a January 2024 start date, 2023/24 academic year fees will apply.

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 should be aware that tuition fees are revised annually and may increase in subsequent years of study. Annual increases will generally reflect UK inflation rates and increases to programme delivery costs.

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Tuition fees

£3,850 (60 credits)

For those intending to study stand-alone modules, the cost will be £1,283 per 20 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

Take a look at our scholarships search for funding opportunities.

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

Back to course

Fees & funding

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

Please note, for courses that have a January 2023 start date, 2022/23 academic year fees will apply. For courses that have a September 2023 and a January 2024 start date, 2023/24 academic year fees will apply.

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 should be aware that tuition fees are revised annually and may increase in subsequent years of study. Annual increases will generally reflect UK inflation rates and increases to programme delivery costs.

Go back
Tuition fees
  • £4,083 (60 credits)
  • £1,361 (20 credits)
Additional costs

International students may have associated visa and immigration costs. Please see student visa guidance for more information.

Available scholarships

Take a look at our scholarships search for funding opportunities.

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

Back to course

Careers

The online MSc in Applied Statistics with Data Science will provide graduates with skills in the statistical analysis of big data. 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

Graduate roles

Typical job roles include:

  • statistician
  • data analyst
  • software developer or engineer
  • statistical programmer
  • data scientist
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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.

Please note there is no deadline for submitting applications.

Start date: Sep 2023

Applied Statistics with Data Science (online)

MSc
part-time
Start date: Sep 2023