Mathematics and Computer Scien - Imperial Colleg

Tuition Fee

Per year

Start Date

2025-10-01

Medium of studying

Duration

48 months

Program Facts

About Program

About University

Admission Requirements

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Contact School

Program Facts

Program Details

Degree

Masters

Major

Computer Science | Software Engineering

Area of study

Information and Communication Technologies

Timing

Full time

Course Language

English

Intakes

Program start date	Application deadline
2025-10-01	-

About Program

Program Overview

This MEng in Mathematics and Computer Science blends core modules from both disciplines, allowing students to specialize with a range of optional modules. The program incorporates a four-month industrial placement, providing practical experience, and culminates in a substantial individual project. Graduates are highly sought after in various industries and academia, with strong transferable skills and specialized knowledge in mathematics and computer science.

Program Outline

Degree Overview:

This MEng in Mathematics and Computer Science is designed for students with a strong interest in both mathematics and computer science. The program aims to provide a solid foundation in pure mathematics, numerical analysis, and statistics, alongside essential computer science concepts, with a focus on software development and theoretical topics. The program is taught jointly by the Departments of Computing and Mathematics, offering a blend of core modules and project work from both disciplines. Students have the flexibility to choose from a wide range of optional modules, allowing them to specialize in areas that align with their interests. The program incorporates a four-month industrial placement in the third year, providing valuable practical experience and real-world exposure. The final year of the program reaches Master's level, offering advanced modules and the opportunity to complete a substantial individual project on a chosen subject. The program caters to the growing demand for professionals with expertise in both mathematics and computer science, as these disciplines are increasingly integrated into various aspects of life.

Outline:

Year 1:

Core Modules:
Graphs and Algorithms
Computing Practical 1
Logic and Reasoning
Analysis 1
Calculus and Applications
Introduction to University Mathematics
Linear Algebra and Groups

Year 2:

Core Modules:
Software Engineering Design
Operating Systems
Computing Practical 2
Probability and Statistics
Optional Modules:
Group A (Computing):
Algorithm Design and Analysis
Models of Computation
Compilers
Symbolic Reasoning
Computing Group Project
Group B (Mathematics Core Modules):
Numerical Analysis
Multivariable Calculus
Linear Algebra
Real Analysis
Complex Analysis
Differential Equations
Group C (Mathematics):
Group Research Project in Mathematics
Groups and Rings
Lebesgue Measure and Integration
Network Science
Partial Differential Equations in Action
Statistical Modelling 1

Year 3:

Core Modules:
Industrial Placement (First Part)
Optional Modules:
Group A (Computing):
Algorithm Design and Analysis
Compilers
Symbolic Reasoning
Models of Computation
Software Engineering Group Project
Networked Systems
Group B (Computing):
Advanced Computer Architecture
Data Processing Systems
Communicating Computer Science in Schools
Graphics
Computer Vision
The Theory and Practice of Concurrent Programming
Custom Computing
Distributed Algorithms
Network and Web Security
Operations Research
Systems Performance Engineering
Robotics
Type Systems for Programming Languages
Databases
Computer Networks and Distributed Systems
Introduction to Machine Learning
Group C (Mathematics):
Numerical Analysis
Multivariable Calculus
Linear Algebra
Real Analysis and Topology
Complex Analysis
Differential Equations
Groups and Rings
Lebesgue Measure and Integration
Network Science
Partial Differential Equations in Action
Statistical Modelling 1
Group D (Mathematics):
Fluid Dynamics 1
Fluid Dynamics 2
Introduction to Geophysical Fluid Dynamics
Asymptotic Methods
Optimisation
Applied Complex Analysis
Dynamics of Learning and Iterated Games
Dynamical Systems
Bifurcation Theory
Geometric Mechanics
Classical Dynamics
Mathematical Biology
Quantum Mechanics 1
Special Relativity and Electromagnetism
Tensor Calculus and General Relativity
Quantum Mechanics 2
Theory of Partial Differential Equations
Function Spaces and Applications
Advanced Topics in Partial Differential Equations
Finite Elements: Numerical Analysis and Implementation
Numerical Solution of Ordinary Differential Equations
Computational Linear Algebra
Computational Partial Differential Equations
Methods for Data Science
Scientific Computation
Probability Theory
Functional Analysis
Fourier Analysis and Theory of Distributions
Markov Processes
Geometry of Curves and Surfaces
Algebraic Curves
Algebraic Topology
Group Theory
Galois Theory
Graph Theory
Group Representation Theory
Formalising Mathematics
Number Theory
Algebraic Number Theory
Statistical Theory
Statistical Modelling 2
Applied Probability
Time Series Analysis
Stochastic Simulation
Survival Models
Introduction to Statistical Learning
Research Project in Mathematics
Stochastic Differential Equations in Financial Modelling
Mathematical Logic
Consumer Credit Risk Modelling

Year 4:

Core Modules:
Industrial Placement for JMC (Second Part)
Individual Project Modules:
Computing Individual Project
Maths Individual Project
Optional Modules:
Group A (Computing):
Computer Vision
Graphics
Custom Computing
Network and Web Security
Advanced Computer Architecture
Operations Research
Type Systems for Programming Languages
Introduction to Machine Learning
Data Processing Systems
Scalable Software Verification
Privacy Engineering
Cryptography Engineering
Scalable Systems and Data
Advanced Computer Graphics
Computational Finance
Reinforcement Learning
Complexity
Software Reliability
Advanced Computer Security
Deep Learning
Principles of Distributed Ledgers
Program Analysis
Software Engineering for Industry
Computational Optimisation
Natural Language Processing
Mathematics for Machine Learning
Modal Logic for Strategic Reasoning in AI
Robot Learning and Control
Scheduling and Resource Allocation
Group B (Mathematics):
Fluid Dynamics 1
Fluid Dynamics 2
Introduction to Geophysical Fluid Dynamics
Asymptotic Methods
Optimisation
Applied Complex Analysis
Dynamics of Learning and Iterated Games
Dynamical Systems
Bifurcation Theory
Geometric Mechanics
Classical Dynamics
Mathematical Biology
Quantum Mechanics 1
Special Relativity and Electromagnetism
Tensor Calculus and General Relativity
Quantum Mechanics 2
Theory of Partial Differential Equations
Function Spaces and Applications
Advanced Topics in Partial Differential Equations
Finite Elements: Numerical Analysis and Implementation
Numerical Solution of Ordinary Differential Equations
Computational Linear Algebra
Computational Partial Differential Equations
Methods for Data Science
Scientific Computation
Probability Theory
Functional Analysis
Fourier Analysis and Theory of Distributions
Markov Processes
Geometry of Curves and Surfaces
Algebraic Curves
Algebraic Topology
Group Theory
Galois Theory
Graph Theory
Group Representation Theory
Formalising Mathematics
Number Theory
Algebraic Number Theory
Statistical Theory
Statistical Modelling 2
Applied Probability
Time Series Analysis
Stochastic Simulation
Survival Models
Introduction to Statistical Learning
Vortex Dynamics
Hydrodynamic Stability
Random Dynamical Systems and Ergodic Theory
Introduction to Stochastic Differential Equations
Stochastic Calculus with Application to Non-Linear Filtering
Algebraic Geometry
Riemannian Geometry
Manifolds
Differential Topology
Complex Manifolds
Commutative Algebra
Lie Algebras
Algebra 4
Elliptic Curves
Bayesian Methods
Machine Learning
Multivariate Analysis
Consumer Credit Risk Modelling
Stochastic Differential Equations in Financial Modelling
Mathematical Foundations of Machine Learning
Analytic Methods in Partial Differential Equations
Mathematical Logic
Group C:
External Course
Communicating Computer Science in Schools

Assessment:

The program utilizes a balanced approach to assessment, incorporating coursework, examinations, and practical components.

Year 1:
10% Coursework
84% Examinations
6% Practical
Year 2:
10% Coursework
57% Examinations
33% Practical
Year 3:
8% Coursework
42% Examinations
50% Practical
Year 4:
9% Coursework
50% Examinations
41% Practical
Programming exercises
Computer-based programming tests
Written coursework
Computer-based coursework
Examinations
Software demonstrations
Group work
Written reports
Research summaries
Oral presentations

Teaching:

The program employs a variety of teaching methods to facilitate learning:

Lectures: Delivering core concepts and theoretical frameworks.
Laboratory-based teaching: Hands-on experience with practical applications and software development.
In-class problem-solving: Encouraging active learning and application of knowledge.

Careers:

Graduates of this program are highly sought after in a range of industries and sectors.

Potential Career Paths:
Management consultancy
Corporations
Computer gaming and special effects
Banking and finance
Academia
Opportunities:
The program equips students with transferable skills relevant to both industry and academia.
Specialized knowledge in mathematics and computer science makes graduates highly competitive in the job market.
Outcomes:
96% of Imperial Computing graduates are in work or further study.
The Department of Computing and Department of Mathematics provide further information about the program.
Students can take a virtual tour of the Department of Computing facilities.

About University

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Imperial College London

Overview:

Imperial College London is committed to achieving excellence in research and education across science, engineering, medicine, and business, aiming to benefit society through its strategic vision. The college leverages its strong disciplinary foundations, collaborative culture, global partnerships, and top-tier ranking to address significant global challenges through its ambitious strategy, "Science for Humanity."

Mission and Values:

Imperial College London's mission is to harness science and innovation for the greater good, focusing on societal impact. The institution emphasizes interdisciplinary collaboration and aims to nurture talent, drive innovation, and tackle global grand challenges. Core values include a dedication to inquiry, precision, and a scientific mindset that drives understanding and transformation.

Unique Approach:

Imperial College London stands out for its commitment to interdisciplinary research and a comprehensive approach to addressing complex global issues. The college's strategy involves creating new cross-institutional Schools of Convergence Science, focusing on climate, AI, health, and space, among other areas. The Imperial Global network will enhance global collaboration to address grand challenges.

Academic Focus:

Imperial College London emphasizes a strong STEMB focus and interdisciplinary research to address complex challenges. The institution fosters connections across various disciplines and sectors to advance scientific knowledge and societal impact.

Student Life:

The college provides an inspiring environment for scientific inquiry and innovation, offering resources and support for students to explore, dream, and ask significant questions. It maintains a culture of discovery and entrepreneurial thinking.

Meaningful Impact:

Imperial College London operates with the agility and forward-thinking of a startup, pursuing breakthrough science with transformative impact. It is recognized as a trusted partner for research and innovation, contributing to the global landscape through its work in London.

Legacy of London:

Situated in a vibrant global city, Imperial College London benefits from London's energy, creativity, and opportunities, reflecting the city's diverse and dynamic character in its global impact.

Total programs

142