Imperial College London

Imperial College London

Master of Science in Computing Science

The Master of Science in Computing Science in Computer and Information Systems is offered by Imperial College London.

Program Length: 1 YEAR.

Master of Science in Computing Science offered by the Imperial College London at the Faculty of Engineering

This course provides intensive training in computer science, and is suitable for graduates of disciplines other than computing who are who are keen to acquire core computing skills or deepen their existing knowledge of computing.


This course provides a year of intensive training in computer science, and is suitable for graduates of disciplines other than computing who are preparing for a career in the computer industry.

Applicants who want to add computing qualifications or to retrain as computing or IT professionals are the natural target for this programme, and the course is also a suitable preparation for PhD studies.

This MSc provides opportunities for postgraduate students to develop and demonstrate knowledge, understanding, and practical skills in basic and specialised topics in computing, as well as the qualities and skills needed for group co-operation and for literature search, technical presentation and report writing. You also have the chance to join in development of a large software project.

Core modules:
You take all of the core modules below.


AI Programming Test (Spring)

C++ Programming Test (Spring)

Computer Architecture (Autumn)
Develops a fundamental understanding of the organisation and operation of a desktop computer system. The emphasis of the module is how high-level language programs are represented and executed at an architectural-level.

Integrated Programming Laboratory (Autumn and Spring)
Practice writing programs and apply methods and techniques learned in other courses in the same term through laboratory sessions, coursework, and constant feedback.

Introduction to C++ Programming (Autumn)
Teaches basic imperative programming concepts, and to gives practical programming experience by solving small-scale problems. Intended for non-Computer Science graduates – no previous programming experience is assumed.

Logic and AI Programming (Autumn)
Introduces logic and formal reasoning through propositional and predicate logic.

MSc Computing Science Individual Project (Summer)

Object Oriented Design and Programming (Autumn)
Introduces the basic object oriented element of program development, and teaches how to design programs in an object oriented manner. Learn how to study programming languages.

Operating Systems (Autumn)
Introduces the organisation and operation of the operating system software that controls the execution of programs and that manages the resources of a computer.

Software Engineering Practice and Group project (Spring and Summer)
Introduces state-of-the-art methods in software engineering. Presenting well-proven techniques for managing real software projects.

Optional modules:

You choose four modules from below.

Advanced Computer Graphics (Spring)
Introduces modern techniques in realistic computer graphics and image synthesis, particularly image-based techniques for photorealism.

Algorithms (Spring)
Introduces algorithm design and analysis, equips you with a range of approaches and established algorithms for solving common classes of problems. Takes a practical approach, focusing on coded examples and applications.

Argumentation and Multi-agent Systems (Spring)
Focuses on the foundations and advances in Multi-Agent Systems, specifically the concepts and implementation techniques required.

Computational Finance (Spring)
Introduces the basic concepts of quantitative finance and financial engineering, including hedging and pricing problems in finance, and how to formulate these problems as mathematical models, and understand the computational techniques to solve the arising models.

Computer Networks and Distributed Systems (Spring)
Details the problems and issues that must be dealt with in constructing robust and flexible distributed applications as well as the underlying network protocols needed to support them.

Cryptography Engineering (Spring)
Teaches how cryptographic techniques can be used to design and implement secure communicating systems for a variety of different needs and applications, and to do so by considering all aspects from theory to more practical issues.

Data Analysis and Probabilistic Inference (Spring)
Aims to teach how probability can be used to make decisions by a computer. Inference networks form a major part of the material along with linear and non-linear methods in statistical pattern recognition.

Databases (Spring)
Introduces database systems, including the relational model, design, languages, transaction management, recovery and concurrency.

Graphics (Spring)
Provides an understanding of basic concepts of computer graphics, and introduces the fundamental mathematical principles used for computer generated imagery, shading and light approximations.

Introduction to Artificial Intelligence (Spring)
An introduction to the fundamental concepts and methods in Artificial Intelligence.

Logic-Based Learning (Spring)
Gives a foundation of knowledge and basic principles of logic-based learning, to develop basic skills in algorithms and heuristics, and to form a logic-based learning task to solve a given learning problem.

Machine Learning (Spring)
Provides the foundations to Machine Learning (ML) and an understanding of basic ML concepts and techniques. Uses Matlab to design, implement and test ML systems.

Network and Web Security (Spring)
Covers network and web security broadly from the network to the application layer. The emphasis of the module is on the underlying principles and techniques, with examples of how they are applied in practice.

Systems Verification (Spring)
Introduces formal methods for system specification and verification. Particular prominence is given to logic-based formalisms and techniques, notably model checking.



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