Statistics and Computational Finance
Mathematical Sciences
Taught
MSc
Statistics and Computational Finance
Taught
MSc
Overview
Application Timeline
Tuition
- Year 1
| Student categories | Study | |
|---|---|---|
| Full-time | Part-time | |
| England | £16,810 | N/A |
| Northern Ireland | £16,810 | N/A |
| Scotland | £16,810 | N/A |
| Wales | £16,810 | N/A |
| Channel Islands | £16,810 | N/A |
| Republic of Ireland | £16,810 | N/A |
| EU | £33,700 | N/A |
| International | £33,700 | N/A |
Requirements
Language requirements information is currently unavailable.
Modules
Advanced regression analysis
CoreMathematical methods of finance
CorePortfolio theory and risk management
Core
About
Programme Overview
This programme is designed to train students to become professional statisticians, focusing on the intersection of statistics and finance while also providing skills applicable in various scientific fields.
Key Focus Areas
- Data Analysis: The programme emphasizes the importance of data analysis across multiple disciplines.
- Contemporary Statistical Methodologies: Students will learn modern statistical ideas and techniques.
- Advanced Knowledge: The curriculum aims to provide in-depth knowledge to enhance competitiveness in various sectors.
Career Prospects
Graduates of this programme will have excellent career opportunities...
Subject Area Information
Statistics is a versatile and essential discipline that involves the collection, analysis, interpretation, presentation, and organization of data. Courses in statistics are designed to equip students with the skills necessary to understand and apply statistical methods to a wide range of real-world problems.
1. Introduction to Statistics
2. Probability Theory
3. Statistical Inference
4. Regression Analysis
5. Multivariate Statistics
6. Time Series Analysis
7. Nonparametric Statistics
8. Statistical Computing
- Understanding of fundamental statistical principles
- Ability to summarize and interpret data
- Proficiency in using statistical software
- Mastery of probabilistic models
- Ability to calculate and interpret probabilities
- Ability to make data-driven decisions
- Proficiency in building and interpreting regression models
- Ability to analyze and interpret multivariate data
- Expertise in analyzing temporal data
- Ability to apply statistical methods that do not rely on parametric assumptions
- Proficiency in statistical programming
- Ability to handle large datasets
Career
Graduates with a degree in statistics have a wide array of career opportunities available to them, including but not limited to roles that involve analyzing data to help organizations make informed decisions, designing experiments, and applying statistical techniques to various fields.
Data Analyst
Analyzing data to help organizations make informed decisions.
Statistician
Designing experiments, conducting surveys, and analyzing data for government agencies, healthcare organizations, and research institutions.
Data Scientist
Using statistical methods and machine learning algorithms to extract insights from large datasets.
Biostatistician
Applying statistical techniques to biological and medical research.
Market Research Analyst
Studying market conditions to identify potential sales opportunities.
Actuary
Analyzing financial risks using mathematics, statistics, and financial theory.
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