Advanced Technology for Financial Computing MSc

Overview of the degree

Advanced Technology for Financial Computing MSc is a 12-month long programme.

This programme is one of a few in the UK to offer an in-depth account of business challenges associated with the confluence of various information technologies (e.g. AI, Data Science, Blockchain, Fintech). Such an integrated perspective is scarce in this area, with most existing technology-oriented programmes focusing on the technical side, with little efforts to clarify the management skills required to ensure the effective exploitation of technological solutions in business settings.

The graduates from this MSc will have both technology and business background to keep up with developments in computing technologies, both in research, engineering for real-world business challenges. Typical areas to pursue a career include Data Scientists for Finance, AI Researchers for Finance, Financial System Architect, Quant Developer, Application Engineer, Financial System Consultant, Software Developer, Data-Driven Technology Researcher (in academia or industry), as well as  technologists of various kinds in banks, insurers, funds, asset management business sectors, government and public sector positions.

Compulsory Courses

The following courses will be compulsory:

• Data-driven Business and Behaviour Analytics
• Informatics Research Review 
• Informatics Project Proposal
• Introductory Applied Machine Learning 
• MSc Dissertation (Informatics) 

Optional courses

Electives can be chosen from courses offered by Informatics, Maths and Business School: 

Digital Business : Transforming Organisations in the Digital Era, Digital Business: Competition in the Age of Platforms, Text Technologies for Data Science, Blockchains and Distributed Ledgers, Natural Computing, Fundamentals of Optimization, Algorithmic Game Theory and its Applications, Large Scale Optimization for Data Science.

Degree Programme Tables

Path programme builder

Informatics MSc Financial Computing Business School courses

• Managing Innovation in Context (CMSE11549): (20 credits, Semester 1) 
• Digital Business: Transforming Organisations in the Digital Era (CMSE11537) (10 credits, Semester 2) 
• Digital Business: Competition in the Age of Platforms (CMSE11536)  ( (10 credits, Semester 2) 
• Technology Entrepreneurship and Commercialisation  (CMSE11576): (10 credits, Semester 2) 
•  Innovation-driven Entrepreneurship-Data Science, Technology and Innovation (CMSE11515): (10 credits, Semester 2)

Informatics MSc Financial Computing CS, Maths and EPCC courses

School of Informatics:

• Blockchains and Distributed Ledgers (INFR11144): (10 credits, Semester 1) 
• Natural Computing (INFR11161): (10 credits, Semester 1) Not Delivered 2024/25
• Algorithmic Game Theory and its Applications (INFR11020): (10 credits, Semester 2)
• Text Technologies for Data Science (INFR11145): (20 credits, full year)

School of Mathematics:

• Finance, Risk and Uncertainty (MATH11088): (10 credits, Semester 1) 
• Fundamentals of Optimization (MATH11111): (10 credits, Semester 1) 
• Credit Scoring  (MATH11148): (10 credits, Semester 2) 
• Financial Mathematics (MATH10003):(10 credits, Semester 2 - strong math foundations, discuss with the Programme Director prior to choosing this course) 
• Large Scale Optimization for Data Science (MATH11147): (10 credits, Semester 2)

EPCC

• Design and Analysis of Parallel Algorithms (EPCC11007):(10 credits, Semester 1) 
• Numerical Algorithms for High Performance Computing (EPCC11006):(10 credits Semester 1) 

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