Data Science (FinTech)
Mathematical Sciences
Taught
MSc
Data Science (FinTech)
Taught
MSc
Overview
Application Timeline
Tuition
- Whole course
| Student categories | Study | |
|---|---|---|
| Full-time | Part-time | |
| England | £11,325 | £11,325 |
| Northern Ireland | £11,325 | £11,325 |
| Scotland | £11,325 | £11,325 |
| Wales | £11,325 | £11,325 |
Requirements
Language requirements information is currently unavailable.
Modules
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About
The MSc degree in Data Science (FinTech) is designed to equip students with a specialized qualification in Data Science within the Financial Technology sector.
Program Highlights
This program offers a comprehensive foundation in Data Science along with an introduction to Financial Technology, essential for careers in the high-demand FinTech industry.
Core Focus Areas
- Investment Analysis
- Algorithmic Trading
- Risk Management
- Decentralised Payment Systems
- Fraud Detection
- Anti-Money Laundering
- Blockchain Design
Curriculum Details
The curriculum includes:
- Principles and practices of Data Science
- Data-driven expertise in payments and
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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