Program Overview

Financial and Computational Mathematics MSc

Course Overview

The course teaches advanced financial mathematics combined with computational techniques. You'll learn mathematical techniques and skills that are used across the financial sector to quantify and hedge risk.

The programme uses modern machine learning techniques for you to learn how to apply them to computational and financial mathematics. This provides hands-on experience using some of the latest software tools and libraries, as used by leading organisations from the industry.

This MSc at Nottingham can provide many career opportunities in sectors including:

• Investment and commercial banking
• Quantitative and actuarial analysis
• Hedge fund management
• Research in academia and industry

Course Structure

The programme includes teaching expertise from the School of Mathematical Sciences and the School of Economics. You will study core modules including:

• Financial Mathematics
• Computational Applied Mathematics
• Scientific Computing and C++
• Choose optional modules such as Statistical Machine Learning or Economics of Corporate Finance to suit your interests.

Why Choose This Course?

• Guest lectures: network with experts from Capital One, Bloomberg, HSBC and PwC
• Top 3 in UK: for research environment
• Scholarships available: to help fund your postgraduate course
• Advisory board: consisting of leading financial industry and academic experts to ensure our MSc is topical

Course Content

The course is made up of 120 credits of taught modules and a 60 credit financial mathematics dissertation.

On arrival to Nottingham, you will be given the opportunity to select your preferred stream from:

• Optional stream 1: Mathematics, Statistics and Computing
• Optional stream 2A: Econometrics
• Optional stream 2B: Microeconomics
• Optional stream 2C: Big data economics

Modules

• Year one
  • Core modules
    • Advanced Financial Mathematics
    • Computational Applied Mathematics
    • Financial mathematics
    • Scientific Computing and C++
  • Optional stream 1: Mathematics, Statistics and Computing
    • Optimization
    • Statistical Foundations
    • Statistical Machine Learning
    • Time Series and Forecasting
  • Optional stream 2A: Econometrics
    • Econometric Theory
    • Financial and Macro Econometrics
    • Game Theory
    • Time Series Econometrics
  • Optional stream 2B: Microeconomics
    • Economics of Corporate Finance
    • Game Theory
    • Microeconomics: Consumer and Firm Behaviour
    • Macroeconomics: Economic Cycles, Frictions and Policy
    • Monetary Theory and Practice
  • Optional stream 2C: Big data economics
    • Big Data Economics
    • Game Theory
    • Machine Learning for Economics

Learning and Assessment

• How you will learn:
  • Lectures
  • Problem classes
  • Independent study
  • Supervision
  • Presentation
  • Computer workshops
  • Research project
  • Group study
• How you will be assessed:
  • Examinations
  • Coursework
  • Reports
  • Programming tasks

Entry Requirements

• Home / UK students:
  • Undergraduate degree: 2:1 in mathematics, physics or engineering
  • Portfolio: Applicants should have a solid background in mathematics including calculus, linear algebra, ordinary differential equations and probability and statistics at degree level.
• EU / International students:
  • Undergraduate degree: 2:1 in mathematics, physics or engineering
  • International and EU equivalents: We accept a wide range of qualifications from all over the world.
  • Portfolio: A strong mathematics background is essential.
  • IELTS: 6.5 (no less than 6.0 in any element)

Fees

• Qualification: MSc
• Home / UK: To be confirmed
• International: To be confirmed

Careers

• Careers advice
• Job prospects
• Partnerships
• Graduate destinations
• Career progression

Related Courses

• Data Science MSc
• Financial Economics MSc
• Finance and Investment MSc
• Statistics MSc