Program Overview

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MSc Applied Statistics in Finance

The MSc 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 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.

Key Facts

• Start date: September
• Accreditation: Royal Statistical Society: MSc graduates may qualify for GradStat status
• Study mode and duration: 12 months full-time

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 studied 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.

Programme Skillset

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

Teaching Staff

Staff member| Research Expertise
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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.
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.
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
Dr Ainsley Miller | 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.
Ryan Stewart | 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.
Andrew Browne | 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.

Course Content

• Throughout your studies, you will take 80 credits of compulsory taught classes, 40 credits of elective taught classes, and in the third (summer) term you'll also undertake your MSc Project (60 credits)
• Programmes terms are as follows:
  • Semester 1 September to December
  • Semester 2 January to April
  • Semester 3 April to July

Compulsory Classes

• 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.
• 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.
• 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.
• Multivariate Analysis
  • 10 credits
  • This module aims to provide you 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.
• 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.
• 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.

Elective Classes

Students are required to take at least 10 credits from List A and the remaining 30 credits can be from List A and/or List B modules.

List A

• 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.
• 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.

List B

• Survey Design & Analysis
  • 10 credits
  • Surveys are an important way of collecting data. This module will introduce you to the methods commonly used to design questionnaires and analyse data resulting from these questionnaires.
• 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.
• Medical Statistics
  • 20 credits
  • This module will cover the fundamental statistical methods necessary for the application of classical statistical methods to data collected for healthcare 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.
• Data dashboards with RShiny
  • 10 credits
  • This module will develop your skills in data presentation and statistical communication. You will learn to develop data dashboards, which are increasingly used to allow key stakeholders (and the public) to gain key insights into data via interactive visualisation.
• 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.
• 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.
• 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.
• Statistical Machine Learning
  • 10 credits
  • This module provides you with the basic theories of machine learning and how to construct a machine model for a real dataset using R. You will also understand the ethical issues regarding data processing and management.

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 by a number of teaching methods:

• lectures (using a variety of media including electronic presentations and computer demonstrations)
• tutorials
• computer laboratories
• coursework
• projects

Assessment

The form of assessment varies from class to class. For most classes the assessment involves both coursework and examinations.

Facilities

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.

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.
• Mathematical knowledge: Applicants are required to have some prior mathematical knowledge, eg A Level or equivalent in:
  • calculus
  • linear algebra
  • differential equations
• English language requirements: You must have an English language minimum score of IELTS 6.0 (with no component below 5.5).

Fees & Funding

• Scotland: £11,900
• England, Wales & Northern Ireland: £11,900
• Republic of Ireland: If you are an Irish citizen and have been ordinary resident in the Republic of Ireland for the three years prior to the relevant date, and will be coming to Scotland for Educational purposes only, you will meet the criteria of England, Wales & Northern Ireland fee status.
• International: £25,500
• Additional costs: If you are an international student, you may have associated visa and immigration costs.

Careers

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.

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

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.