- Start date: September
- Accreditation: On successful completion of the MSc, students may be eligible for GradStat status.
- Study mode and duration: 12 months full-time
Study with us
- a conversion course 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
- become equipped with the necessary skills to work as an applied statistician in sectors such as insurance, finance and commerce
Why this course?
Our 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 studies statistics before. You will be supported 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.
Students will 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
On the MSc Applied Statistics in Finance 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
- 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
|Staff member||Research Expertise|
|Dr Neil Banas||An oceanographer and mathematical ecologist, with a background in physical oceanography. Current research investigating how climate change affects marine ecosystems and the role of biological complexity (diversity, adaptability, behaviour, life history) in large-scale patterns in the ocean.|
|Dr Bingzhang Chen||Current research is on how biodiversity affects marine ecosystem functioning such as primary production and biological carbon pump, for which the primary producers particularly phytoplankton play the pivotal role.|
|Dr Alison Gray||Research interests cover pattern recognition and machine learning, image analysis, applied epidemiology, SDE models for epidemics, and applications of statistics for honey bee research.|
|Prof David Greenhalgh||Research interests include mathematical and statistical techniques applied to biological problems, in particular mathematical and statistical modelling in epidemiology.|
|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.|
|Prof Chris Robertson||Professor of Public Health Epidemiology in the Department of Mathematics and 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 design, conduct and analysis of medical research studies.|
|Dr Jiazhu Pan||Main research interests are Time Series Analysis, Financial Econometrics and Multivariate Analysis.|
|Prof Xuerong Mao||Research interests are in the areas of stochastic differential equations and their applications in finance, engineering, population systems and ecology|
Research focuses on applications of statistics to health and medical datasets, with a particular interest in spatio-temporal modelling. Involved in consultancy involving data visualisation and data dashboard development.
Teaching Associate with interests in mathematics and statistical pedagogy, in particular easing the transition from school to university and understanding the mental health struggles of students. Member of the core team of the Scottish Qualification Authority's Higher Applications of Mathematics course. Qualified Mental Health First Aider and Sexual Assault First Responder who runs a support service for all mathematics and statistics students.
Teaching Associate with interest in statistical pedagogical research. Statistical expertise in the linkage and analysis of large administrative datasets in the field of public health epidemiology and policy. Member of Higher Education Academy.
Previous research experience includes analysis of data from clinical trials, observational studies, and systematic reviews. Teaching and pedagogical interests focus on the teaching of statistics to those from other disciplines.
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.
Postgraduate Taught Director, Department of Mathematics and Statistics
Semester 1 modules are compulsory. In Semester 2 you are required to take 40 credits of optional modules, in addition to 20 credits of compulsory modules.
With the approval of the Course Director, students may substitute other appropriate modules offered by the University for one or more of the optional modules listed below.
In semester 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.
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
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
Experimental Design (10 credits)
This module provides students with the fundamental principles of statistical modelling through experimental design. The statistical models used in the analysis of balanced experimental designs are derived and used in the analysis of data sets.
You will cover topics such as:
- Completely Randomised Design
- Randomised Block Experiments
- Factorial Experiments and Interactions
- Nested Designs and Repeated Measures Designs
Multivariate Analysis (10 credits)
This module aims to provide students with a range of applied statistical techniques that can be used in professional life to analyse multivariate data. Both statistical and machine learning approaches are included.
You will cover topics such as:
- Graphical methods for investigating multivariate data
- Logistic Regression and Discrimination
- Linear and Quadratic Discriminant Analysis
- Non-parametric Classification
- Hierarchical and non-Hierarchical Clustering
- Principal Component Analysis
In semester 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 finance, risk analysis and medical research.
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
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
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
- 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
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
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
Business Analytics (10 credits)
Every two days, we generate as much data as the data generated in all human history up to 2003. From online data on every click of the mouse on the internet through the huge upsurge in manufacturing companies’ use of sensors to sports organisations collecting in game data. With these increased quantities of data comes an increased need for tools to make sense of the main messages coming from these data.
The class will build on the fundamental multivariate statistics by developing both visualisation and advanced analysis techniques relevant in the area of big data. The focus will be on application and interpretation of techniques and there will be an investigation of what makes good data. The class will develop both new theoretical knowledge in the form of analytics techniques as well as new software skills in relevant analytics software.
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.
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.
Learning & teaching
Classes are delivered by a number of teaching methods:
- lectures (using a variety of media including electronic presentations and computer demonstrations)
- computer laboratories
Teaching is student-focused, with students encouraged to take responsibility for their own learning and development. Classes are supported by web-based materials.
The form of assessment varies from class to class. For most classes the assessment involves both coursework and examinations.
- The assessment will take ask you to demonstrate your statistical knowledge and skills to analyse real-world data and interpret the results in the context of the research question
- 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
The Department of Mathematics & Statistics has teaching rooms which provide you with access to modern teaching equipment and access to University computing laboratories, with all necessary software available.
You'll also have access to a common room facility which gives you a modern and flexible area for individual and group study work, and is also a relaxing social space.
The 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, including APHA, Public Health and Intelligence (Health Protection Scotland), Greater Glasgow and Clyde Health Board and the Marine Alliance for Science and Technology Scotland (MASTS). We bridge the gap between academia and real-life. Our research has societal impact.
We've a thriving international community with students coming here to study from over 100 countries across the world. Find out all you need to know about studying in Glasgow at Strathclyde and hear from students about their experiences.Visit our international students' section
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.
Applicants are required to have some prior mathematical knowledge, eg A Level or equivalent in:
If you have any questions, email firstname.lastname@example.org.
|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
All fees quoted are for full-time courses and per academic year unless stated otherwise.
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.
|England, Wales & Northern Ireland|
If you are an international student, you may have associated visa and immigration costs. Please see student visa guidance for more information.
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.
We work closely with the University's Careers Service. They offer advice and guidance on career planning and looking for and applying for jobs. In addition they administer and publicise graduate and work experience opportunities.
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.”
Graduates from the MSc Applied Statistics programme have gone on to be employed in a number of different sectors such as:
- Clinical Trials Statistician at Usher Institute
- Data Analyst at Bending Spoons
- Associate Statistician at Thermo Fisher Scientific
- Biostatistician at Optical Express
- Statistician at Phastar (x7)
- Statistician at Quotient Sciences (x3)
- Information Analyst at NHS Scotland
- Statistician at Scottish Government (x4)
- Statistician at Abbots Diabetes Care
- Medical Statistician at University of Oxford
- Credit Risk Analyst at Clydesdale Bank
- Statistical Analyst at Medpace
- Data Scientist at Scottish Water
- PhD studentship in social sciences