King's College London

How you will learn

On the Quantitative Mathematics BSc, you will learn mathematics by doing mathematics: exploring definitions, testing ideas, building arguments, using computation, working with others and learning how to communicate your reasoning clearly.

Teaching is designed to be active and collaborative. Alongside lectures, tutorials and independent study, you will take part in seminar-style mathematical sessions, coding and computational work, modelling projects, group problem-solving, presentations and reflective activities. This approach is intended to help you develop both mathematical depth and the confidence to use mathematics in unfamiliar situations.

As you progress through the degree, you will move from carefully structured foundations to increasingly open-ended modelling tasks. You will learn how to frame problems, make assumptions, build and test models, analyse data, use computational tools and explain your conclusions to different audiences.

You will spend approximately 20% of your time in Lectures and 10% in Tutorials and Labs. The remaining 70% of your time will be on self-guided study, including completing projects in the Quantitative Modelling modules. This estimate is across the entire programme and therefore weightings will vary year to year and are dependent on optional modules selected. You will also be allocated a Personal Tutor, who will support your academic progress, wellbeing and wider development during your time at King’s.

The Shape of the Degree

Courses are divided into modules. You will normally take modules totalling 360 credits across three years. 

• In Year 1, all 120 credits are compulsory. This gives every student a strong shared foundation in university mathematics, computation, probability and quantitative modelling.
• In Year 2, you take 60 credits of compulsory modules and 60 credits of optional modules. This allows you to continue the core quantitative mathematics pathway while beginning to shape the degree around your interests.
• In Year 3, you take 30 credits of compulsory modules and 90 credits of optional modules. This gives you significant flexibility to specialise in areas such as pure mathematics, probability and statistics, mathematical finance, complex systems, networks, mathematical biology, applied mathematics or related fields, subject to module availability and prerequisites.

Foundational Mathematics Modules

Your first year includes two substantial 30-credit modules: Mathematical Foundations and Mathematical Methods and Proofs. Together, these modules build the language, structures and habits of university mathematics.

You will develop confidence with proof, abstraction, sets, functions, sequences and series, linear algebra, calculus, differential equations and formal reasoning. The emphasis is not only on learning mathematical content, but on learning how mathematicians think: how to reason from definitions, test examples, build arguments and communicate ideas precisely.

These modules are designed for students with A-level Mathematics, whether or not you have studied Further Mathematics. They provide a rigorous foundation for later study in pure mathematics, applied mathematics, statistics, computation and quantitative modelling.

Quantitative Modelling Modules

Quantitative modelling is the practical spine of the degree. Across all three years, you will learn how to turn complex, real-world questions into mathematical and computational models, how to test and refine those models, and how to communicate what your results mean.

The journey begins with Foundations of Quantitative Modelling, where you learn to translate messy real-world problems into structured mathematical briefs, identify assumptions, implement simple computational models and present your reasoning clearly. Later modules develop systems modelling, simulation, optimisation, data-driven modelling, machine learning, responsible data practice and project work.

The final stage is an advanced project in quantitative modelling, completed from an externally defined brief and developed to professional standards. This gives you the opportunity to bring together mathematics, computation, data analysis, communication and teamwork in a substantial real-world modelling project.

Computation, Statistics and Data Modules

Modern quantitative work depends on computation and data as well as mathematical theory. Alongside the Quantitative Modelling modules, you will study programming, numerical methods and statistical modelling.

Computational Methods for Mathematics introduces programming for mathematical contexts, including Python, core programming constructs, data structures and mathematical libraries. Numerical and Computational Methods develops your understanding of numerical algorithms, stability, accuracy, convergence and diagnostic reasoning. Statistical Modelling introduces common statistical methods, including linear models, regression analysis and analysis of variance, with applications in a range of fields.

Together, these modules help you build the practical fluency needed to use mathematics in real quantitative settings: writing code, analysing data, testing models, interpreting outputs and explaining limitations.