Postgraduate Studentships - Search for funding opportunities.
Postgraduate Studentships - Search for funding opportunities.
Study Details: Online across 24 or 36 months, part-time with both September and January entry points.
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’ll be supported through this part-time programme by members of staff who work directly with industry to develop skills which are relevant to current areas of research. You’ll gain skills in:
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. They 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.
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
For fees and funding options, please visit website to find out more
The online MSc in Applied Statistics in Finance will provide graduates with skills in the statistical analysis of data from a wide range of disciplines. These skills are required by many employers in sectors such as:
Compulsory classes
Foundations of Probability & Statistics (20 credits)
Data Analytics in R (20 credits)
Statistical Modelling & Analysis (20 credits)
Financial Econometrics
Financial Stochastic Processes
Research project (60 credits)
Elective classes
You're required to take 40 credits of elective classes.
Data dashboards with Rshiny (10 credits)
Quantitative Risk Analysis
Survey Design & Analysis (10 credits)
Medical Statistics (20 credits)
Effective Statistical Consultancy (10 credits)
Bayesian Spatial Statistics (10 credits)
Machine Learning for Data Analytics (20 Credits)
Big Data Fundamentals (10 credits)
Big Data Tools & Techniques (10 credits)
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