Financial Technology (Computer Science)
Mathematical Sciences
Taught
MSc
Overview
Application Timeline
Tuition
- Year 1
| Student categories | Study | |
|---|---|---|
| Full-time | Part-time | |
| England | £12,125 | £12,125 |
| Northern Ireland | £12,125 | £12,125 |
| Scotland | £12,125 | £12,125 |
| Wales | £12,125 | £12,125 |
| Channel Islands | £12,125 | £12,125 |
| Republic of Ireland | £12,125 | £12,125 |
| EU | £23,500 | £23,500 |
| International | £23,500 | £23,500 |
Requirements
Language requirements information is currently unavailable.
Modules
Modules is currently unavailable
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About
Introduction
In today's competitive financial industry, professionals need more than just software skills or basic market analysis. A comprehensive understanding of finance, economics, and advanced computing is essential.
Course Objectives
The MSc Financial Technology (Computer Science) course aims to equip students with:
- Understanding of financial and economic market intricacies
- Ability to apply advanced computing methods
- Skills to design and program solutions that integrate finance, economics, and computation
Course Content
The curriculum includes a focus on:
- Financial markets
- Big data analytics
- Blockchain technology
- Artificial
Subject Area Information
Financial mathematics is a specialized field that combines mathematical theories and methods with financial practice to solve problems in finance. This discipline is essential for understanding and modeling financial markets, managing financial risks, and making informed investment decisions.
1. Introduction to Financial Mathematics
2. Probability and Statistics for Finance
3. Derivatives and Risk Management
4. Fixed Income Securities
5. Portfolio Theory and Asset Pricing
6. Numerical Methods in Finance
7. Financial Econometrics
8. Advanced Topics in Financial Mathematics
- Understanding fundamental financial concepts
- Calculating present and future values
- Analyzing financial data
- Modeling uncertainty
- Valuing derivative securities
- Designing hedging strategies
- Valuing fixed income securities
- Constructing optimal portfolios
- Implementing numerical algorithms
- Analyzing financial time series data
- Understanding complex financial instruments
- Proficiency in programming languages such as Python, R, or MATLAB
Career
Graduates of financial mathematics programs can pursue a variety of careers in the finance industry, leveraging their strong foundation in mathematics, finance, and computational techniques.
Quantitative Analyst (Quant)
Developing and implementing complex financial models to support trading and risk management.
Risk Manager
Identifying and mitigating financial risks for banks, investment firms, and corporations.
Financial Engineer
Designing new financial products and strategies using advanced mathematical techniques.
Investment Analyst
Analyzing financial data to make investment recommendations.
Actuary
Assessing financial risks in the insurance and pension industries.
Data Scientist
Applying statistical and computational methods to analyze financial data and inform business decisions.
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