MSc Financial Analytics

at Queen's University Belfast

Modules:
 

• Computational Methods in Finance
• Dissertation- MSc Financial Analytics
• Advanced Analytics & Machine Learning
• Applied Research Project
• Advanced Financial Data Analytics
• Financial Data Analytics

• Normally a strong 2.2 Honours degree (with minimum of 55%) or equivalent qualification acceptable to the University in Finance, Mathematics, Economics or other relevant quantitative subject. Science and Engineering disciplines will be considered where there is a significant mathematical component. Performance in relevant modules must be of the required standard. Applicants with a 2.2 Honours degree (scoring below 55%) or equivalent qualification acceptable to the University and sufficient relevant experience will be considered on a case-by-case basis.
   

The cost of studying at Queen's University Belfast varies largely depending on the type of university and the degree chosen. More often than not, humanities, arts, and education courses are less expensive than degrees in medical, engineering, or management. Furthermore, students who choose to study at the postgraduate level typically pay higher tuition fees, which again vary depending on the programme chosen.

Most universities provide scholarships or financial aid to students in order to help them offset their tuition costs. Students should be aware that additional course fees and incidental expenses may not be included in tuition. On average, international students can expect to pay £25800 annually, to study in Queen's University Belfast.

This programme will equip students with cutting-edge quantitative and computational techniques and strategies used by leading finance and financial technology (FinTech) firms. Today, all full-service finance and business consulting firms employ Financial Analytics professionals in their operations as do many boutique firms, such as asset managers and hedge funds. Furthermore, many IT software organisations are attracted to graduates from this programme due to their specialism at the interface between computing, data analytics and finance.