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Different course options

Study mode

Full time

Duration

1 year

Start date

23-SEP-24

Key information
DATA SOURCE : IDP Connect

Qualification type

MSc - Master of Science

Subject areas

Computing Methodologies Finance / Accounting (General) Statistics

Course type

Taught

Course Summary

Overview

Our Computational Finance MSc will introduce you to the computational methods that are widely used by practitioners and financial institutions in today’s markets. You will get a solid foundation not only in traditional quantitative methods and financial instruments, but also scientific computing, numerical methods, high-performance computing, distributed ledgers, big-data analytics and agent-based modelling. These techniques will be used to understand financial markets from a post-crisis perspective which incorporates findings from the study of financial markets at high-frequency time scales, modern approaches to understanding systematic risk and financial contagion, and disruptive technologies such as distributed-ledgers and crypto-currencies. The course is highly practical, and you'll have the opportunity to apply your learning to real-world data and case studies in hands-on laboratory sessions.

Course detail

Our course provides an understanding of modern financial technology (FinTech) including electronic trading and distributed-ledger technology. You will gain practical hands-on techniques for working with and analysing financial data, which draw on modern developments in Artificial Intelligence and Big Data technology. There will be opportunity to understand the practical aspects of quantitative finance and FinTech from Industry experts located in the heart of one of the World’s financial centres.

Teaching and assessment

We use lectures and group tutorials to deliver most of the modules on the course. You will also be expected to undertake a significant amount of independent study. Typically, one credit equates to 10 hours of work., e.g. 150 hours work for a 15-credit module. These hours cover every aspect of the module, including independent study. The primary method of assessment for this course is a combination of written examinations, coursework, in-class tests, individual projects and oral presentations. The individual project will be assessed through a dissertation.

Career prospects

The Careers Service run tailored sessions for Informatics students and a careers programme which includes skills sessions and visits from top employers. Some graduates work in cyber security companies, general software consultancy companies, specialised software development businesses and the IT departments of large institutions (financial, telecommunications, and public sector). Recent employers include Hang Seng Bank, Lloyds Banking Group and Merrill Corporation. Other graduates have entered into the field of academic and industrial research in areas such as machine learning, software engineering, algorithms and computer networks.

Modules

Scientific Computing for Finance (15 Credits)
High-Frequency Finance (15 Credits)
Quantitative Methods in Finance (15 Credits)
Agent Based Modelling in Finance (15 Credits)
Industry - Expert Lectures in Finance (15 Credits)
Individual Project (60 Credits)

Tuition fees

UK fees
Course fees for UK students

For this course (per year)

£12,468

International fees
Course fees for EU and international students

For this course (per year)

£37,368

Entry requirements

A Bachelor's degree with a high (minimum of 65%) 2:1 honours (or international equivalent) in Computer Science or another relevant quantitative discipline (such as Mathematics, Statistics, Physics, Natural Science, Electronic Engineering, General Engineering, Operations Research, or a joint degree in two such subjects). Applicants should also have a sound background in basic mathematics, in particular familiarity with standard concepts of calculus, trigonometry, linear algebra, vectors and matrix mathematics. In addition, applicants should be competent in computer programming, close to the level expected at the end of the first year of a BSc honours degree in computer science.