Computer Science and Mathematics, BSc (Hons)

In a rapidly evolving world, computer science and mathematics drive technological innovation, requiring strong foundations in mathematical fundamentals, statistics, and machine learning. The BSc in Computer Science and Mathematics at Durham explores their interconnections, offering practical skills for data-driven careers through industry collaborations. The program balances core modules in both fields during the first year, including computational thinking, algorithms, calculus, linear algebra, probability, and statistics.In subsequent years, students select from optional modules on topics like artificial intelligence, data science, and statistical modelling, culminating in a final-year individual project. This flexible structure allows transfers to related degrees and develops transferable skills for roles in emerging fields. Assessment combines coursework, exams, and the project, which constitutes a third of final-year marks, preparing graduates for impactful careers in technology.

Year 1 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. In recent years, option modules have included: Computer Systems Programming Year 2 All second-year modules are chosen from options. In recent years option modules have included: Theory of Computation Programming Paradigms Artificial Intelligence Data Science Probability Statistical Modelling Statistical Inference Data Science and Statistical Computing Algebra. Year 3 (Year 4 if undertaking a placement or year abroad) You can choose from several opportunities for the final-year individual Project. Using the knowledge and tools from the course you will complete either a detailed and critical examination of a relevant area of computer science; investigate a topic of interest; or perform an in-depth analysis of a data set under the supervision of a member of academic staff. The project further develops your skills and ability to plan and manage your own learning, and then present your findings and conclusions. The remainder of the year is made up of option modules, which in recent years have included: 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.