Computer Science and Mathematics

The MSci Computer Science and Mathematics degree is meticulously crafted to equip students with the foundational knowledge and practical skills required to thrive in the constantly changing data-driven workplace.

In our rapidly evolving world, the field of computer science stands as a driving force behind transformative technological breakthroughs that have reshaped our lives for the better. The skills demanded by today's dynamic tech sector demand a strong knowledge in mathematical fundamentals, statistics, and machine learning. During the first two years you will follow the same course structure as the BSc with an equal balance of Computer Science and Mathematics to ensure that the programme provides students with the essential foundation. From the third year, the MSci introduces a wider range of modules and an advanced project in the final year.

Cap off your academic journey with a choice between undertaking an advanced project, where you tackle a theoretical area or an applied problem in-depth , or our distinctive interdisciplinary enterprise module where you can delve into an area that resonates with your passions and leverages the skills honed within the course.

You will be taught by passionate researchers with diverse areas of expertise such as artificial intelligence, data science, fundamental algorithms, statistical uncertainty, statistical uncertainty, applied statistics for large-scale applications and applied algebra. The final-year advanced project is an opportunity to fully engage with the cutting-edge research being carried out in the departments.

State-of-the-art facilities further enhance the learning experience, providing students with access to cutting-edge resources and opportunities to engage in innovative research projects.

The Year 1 curriculum provides a foundation in the fundamentals of computer science, pure mathematics, statistics, and probability providing robust groundwork for advanced study in subsequent years.

Year 2 allows students to embark on an exploration of a diverse array of innovative technological applications. At the same time, this can be underpinned with a deep mathematical grounding in statistics and machine learning to provide the foundation on their academic journey for more advanced topics in statistics, artificial intelligence and data science or an alternative grounding in abstraction with applications in theoretical computer science.

The flexible structure, coupled with an array of optional modules, empowers you to tailor your academic trajectory in alignment with your evolving interests. Whether completing the programme in three years, opting for a placement year, or embarking on a year abroad, the choice is yours.

Course structure

Year 1 modules

Core modules:

Computational Thinking

explores the breadth of computer science and the ways in which the different topics are linked. You will learn the importance of computation and computational thinking in the modern world and the impact it has on technology advances in different settings.

Algorithms and Data Structures

introduces the theory and practice of problem-solving in computing through the development of algorithms and their associated data structures, for common computer science problems. Content included machine modelling, control flow structures and analysis of algorithms.

Calculus

builds on ideas of differentiation and integration in A level mathematics. The module begins with functions of a single variable before moving on to functions of several variables. Topics include methods of solving ordinary and partial differential equations, and an introduction to Taylor series, Fourier series and Fourier transforms.

Linear Algebra

presents mathematical ideas, techniques in linear algebra and develops the geometric intuition and familiarity with vector methods you will need for more challenging material later in the course.

Probability

introduces mathematical ideas on probability in preparation for more demanding material later in the course. The module builds problem solving skills through the exploration of a mathematical subject of relevance to the real-world (applied) that is based on rigorous mathematical foundations (pure).

Statistics

introduces frequentist and Bayesian approaches and demonstrates the relevance of these principles and procedures using real problems. This introductory module lays the foundations for all subsequent study of statistics.

Optional modules:

• Computer Systems
• Programming.

Year 2 modules

Optional modules:

• Theory of Computation
• Programming Paradigms
• Artificial Intelligence
• Data Science
• Probability
• Statistical Modelling
• Statistical Inference
• Data Science and Statistical Computing
• Algebra

Year 3 modules

Core modules:

Project Preparation

You will work on essential research skills including researching a topic, writing, presenting, and begin preparation for the final-year Advanced Project in an area of your choice.

Optional modules:

• Algorithmic Game Theory
• Bioinformatics
• Deep Learning
• Human-AI Interaction Design
• Recommender Systems
• Reinforcement Learning
• Natural Computing Algorithms
• Advanced Statistical Modelling
• Bayesian Computation and Modelling
• Cryptography and Codes
• Decision Theory
• Machine Learning and Neural Networks
• Mathematical Finance
• Operations Research
• Stochastic Processes
• Science Enterprise

Year 4 modules

Core modules:

Either the Advanced Project in Computer Science or Mathematics Project

Your final-year project is a detailed and critical examination of a relevant area of computer science, mathematics or statistics. It is possible that the resulting research might be published in a journal or at a conference, possibly as a prelude to higher level studies.

Optional modules:

• Advanced Algorithms
• Applied Cryptography
• Blockchain and Cryptocurrencies
• Coding and Information Theory
• Learning Analytics
• Natural Language Processing
• Quantum Computing
• Randomised Algorithms and Probabilistic Methods
• Representation Theory
• Topics in Combinatorics
• High-Dimensional Statistics
• Uncertainty Quantification
• Nonparametric Statistics
• Deep Learning and Artificial Intelligence

Additional Pathways

Students can apply to be transferred onto either the with  Year Abroad or with Placement pathway. Places on these pathways are in high demand and if you are chosen your studies will extend from three years to four or four to five depending on course.