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BSc Mathematics with Finance

Newcastle University

Institution
Newcastle University
Level
undergraduate
Subject
Mathematics with Finance
Duration
3 years
UCAS code
G1N3
Typical offer
A-level AAB, IB 34

Entry requirements

A level: AAB IB: 34 points

About this course

BSc Honours in Mathematics and Accounting and Finance. Core topics include Statistics, Mathematical Modelling, Financial Analysis, Accounting, Programming, and Artificial Intelligence & Machine Learning. The majority of modules are in mathematics and statistics, supplemented by accountancy and corporate finance. Financial modules include Asset Pricing and Portfolio Investment and Money and Banking.

Modules

  • Introduction to Financial Accounting
  • Introduction to Management Accounting and Finance
  • Introductory Algebra
  • Real Analysis
  • Introduction to Probability and Statistics
  • Introductory Calculus and Differential Equations
  • Multivariable Calculus
  • Responsible Corporate Finance
  • Statistical Inference
  • Regression
  • Interpreting Company Accounts
  • Frontiers in Data Science A
  • Asset Pricing and Portfolio Investment
  • Money and Banking
  • Linear Algebra
  • Complex Analysis
  • Groups and Rings
  • Curves and Surfaces
  • Coding Theory
  • Numerical Methods with Python
  • Stochastic Processes
  • Data Visualisation
  • Probability
  • Principles of Quantum Mechanics
  • Vector Calculus
  • Differential Equations, Transforms and Waves
  • Fluid Dynamics I
  • Mathematical Biology
  • Case Studies in Finance, Accounting and Business
  • International Financial Management
  • Stochastic Financial Modelling
  • Clinical Trials
  • Decision Modelling for Health Data Science
  • Topics in Medical Statistics and Health Data Science
  • Curves and Surfaces
  • Coding Theory
  • Numerical Methods with Python
  • Global Education in Mathematics and Statistics
  • Group Theory
  • Linear Analysis
  • Matrix Analysis
  • Metric Spaces and Topology
  • Number Theory and Cryptography
  • Matrix Representations of Groups
  • Experimental Design
  • Foundations of Machine Learning
  • Extreme Value Theory
  • Time Series
  • Survival Analysis
  • Statistical Genetics
  • Mathematical Statistics
  • Statistical Modelling
  • Bayesian Statistics and Decision Theory
  • Markov Processes
  • Principles of Quantum Mechanics
  • Mathematical Biology
  • Advanced Quantum Mechanics
  • Classical Fields
  • Quantum Information
  • Methods for Differential Equations
  • Fluid Dynamics II
  • Relativity and Fundamental Particles
  • Hydrodynamic and Climate Instabilities
  • Variational Methods and Lagrangian Dynamics
  • Career Development for final year students