MSc Applied Statistics in Finance (online)

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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, the analysis and manipulation of complex data, and use of statistical software packages
  • learn to interpret and report the results from data analyses
  • become equipped with the necessary skills to work as an applied statistician in sectors such as insurance, finance and commerce
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Why this course?

Our online MSc in Applied Statistics in finance 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. Over the three years, our students gain skills in:

  • problem solving
  • analysis and modelling of financial data
  • the use of statistical software for data analysis and reporting
  • effective communication of statistics

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 in areas such as insurance, finance and commerce.

Programme skillset

On the online Applied Statistics in Finance MSc programme you'll have the opportunity to acquire:

  • an 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, particularly related to the financial sector
  • 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

Mathematics class

THE Awards 2019: UK University of the Year Winner

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, compulsory modules with a financial focus are undertaken. 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.

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.

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Course content

  • In Year 1 all classes are compulsory, this amounts to 60 credits
  • In Year 2 you are required to take 20 credits of compulsory classes and 40 credits of optional classes
  • In Year 3 you will 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 modules listed below.

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

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

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

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 will 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
  • 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 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

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

Electives

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

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
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 will 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
  • machine learning algorithms
  • understand when to apply which algorithm
  • deep Learning
  • artificial neural networks

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

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, 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.

Pre-Masters preparation course

The Pre-Masters Programme is a preparation course held at the University of Strathclyde International Study Centre, for international students (non EU/UK) who do not meet the academic entry requirements for a Masters degree at University of Strathclyde. The Pre-Masters programme provides progression to a number of degree options.

Upon successful completion, you'll be able to progress to this degree course at the University of Strathclyde.

Back to course

Fees & funding

The MSc in Applied Statistics in Finance (online) consists of 180 credits studied over three years. You'll study 60 credits annually. For those intending to study stand-alone modules, the cost will be £1,283 per 20 credit module payable on registration.

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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Scotland

£3,850 (60 credits)

England, Wales & Northern Ireland

£3,850 (60 credits)

International

£3,850 (60 credits)

Scholarships

Take a look at our available scholarships.

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.

Back to course

Fees & funding

The MSc in Applied Statistics in Finance (online) consists of 180 credits studied over three years. You'll study 60 credits annually. For those intending to study stand-alone modules, the cost will be £1,283 per 20 credit module payable on registration.

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
Scotland
  • £4,083 (60 credits)
  • £1,361 (20 credits)
England, Wales & Northern Ireland
  • £4,083 (60 credits)
  • £1,361 (20 credits)
International
  • £4,083 (60 credits)
  • £1,361 (20 credits)
Scholarships

Take a look at our available scholarships.

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.