- Start date: September
- 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
- 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?
This is a conversion course, designed for candidates from a broad background of disciplines. Students will gain skills in:
- problem solving
- analysis and modelling of financial data
- the use of statistical software for data analysis and reporting
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
What you'll study
You'll take modules to equip you with fundamental statistical and data analysis skills.
Compulsory modules with a financial focus are undertaken, as well as elective modules, which allows you to tailor the course to your own interest areas.
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.
On successful completion of the MSc, students 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.
- 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 modules offered by the University for one or more of the optional modules listed below.
The following modules are worth 20 credits each:
Foundations of Probability & Statistics
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.
Data Analytics in R
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.
Statistical Modelling & Analysis
This class 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.
The following modules are worth 10 credits each:
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
The class 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.
The following modules are worth 10 credits each:
Quantitative Risk Analysis
Survey Design & Analysis
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.
Bayesian Spatial Statistics
This class will introduce you to Bayesian statistics and the modern Bayesian methods that are used in a variety applications. Like with other classes, the focus is on real-life data and using statistical software packages for analysis.
Effective Statistical Consultancy
This class 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.
The following module is worth 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.
Machine Learning for Data Analytics
The aim of this class is to equip you with a sound understanding of the principles of machine learning and a range of popular approaches, along with the knowledge of how and when to apply the techniques. The class balances a solid theoretical knowledge of the techniques with practical application via Python (and associated libraries) and students are expected to be familiar with the language. Aspects of the course will be highly mathematical and technical requiring strong math and programming ability (Python and Tensorflow).
The following module is worth 60 credits:
Learning & teaching
Classes are delivered using Myplace, our online teaching environment.
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 will 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 varies for each class, but all will be undertaken online. For most classes the assessment involves both coursework and examinations. Coursework will typically involve a project where you will analyse data, write code, interpret statistical output and produce a report. Group work may be undertaken in some classes.
Minimum second-class 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 email@example.com.
|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.
Fees & funding
The MSc in Applied Statistics in Finance (online) consists of 180 credits studied over three years. Students will study 60 credits annually.
For those intending to study stand-alone modules, the cost will be £1250 per 20 credit module payable on registration.
Please note: the fees shown are annual and may be subject to an increase each year. Find out more about fees.
This course will provide graduates with skills in the statistical analysis of financial data. These skills are required by many employers in sectors such as:
- investment companies
- financial institutions
- government organisations
- internet information providers
Typical job roles include
- data analyst
- software developer or engineer
- statistical programmer
- data scientist
- decision scientist