BSc (Hons) Mathematics and Statistics with Data Science with a Placement Year Bachelor's degree at the University of Reading

Explore mathematical analysis concepts including inequalities, sequences, series and functions.

Extend your existing knowledge of calculus into two or more dimensions, exploring techniques of ordinary differential equations of the first and second order, and learn programming with mathematical applications.

Gain a solid introduction to fundamental topics in mathematics and develop the necessary skills to study mathematics at university-level. You'll focus on the concept of sets, functions and various familiar number systems, as well as the importance of proofs and how to construct them.

Learn how to solve systems of linear equations, determine eigenvalues and eigenvectors, and develop the algebra of matrices which are used as a stepping-stone to the more general theory of linear and inner-product spaces.

Discover the importance of expressing mathematical and statistical concepts and results clearly, logically and concisely, and how to implement basic problem-solving strategies – including those in data science.

Understand probability and probability distributions, and techniques for statistical inference and data science – such as regression and hypothesis testing.

Build on your knowledge of ordinary differential equations and explore partial differential equations and their applications. You'll explore non-constant coefficients, integral and series solutions, Fourier series, the theory of boundary value problems, diffusion equations, wave equations and Laplace's equation.

Gain understanding of the most common models, including multiple linear regression and completely randomised designs, and explore the key principles of planned experiments. Learn how models are applied to practical problems and gain experience of real-life data analysis.

Uncover the interplay between probability theory and fundamental areas of mathematics, and formulate general real or abstract problems in a probabilistic model. Interval estimation and hypothesis testing are also developed.

Describe, analyse and implement numerical methods for problems in continuous mathematics, including solution of linear equations and nonlinear scalar equations, interpolation, scalar optimisation and solution of ordinary differential equations.

Develop your problem-solving and independent research skills by applying mathematical modelling techniques to solve real-world problems across a broad range of scientific, engineering and economical areas. You'll also expand your team-working, presentation, career management, technical, verbal and written communication skills.

Understand the concepts of abstract algebra, including groups, rings and fields and the connections between them.

Using Python programming languages, develop your skills and knowledge to use current tools in general program designs, development and data science. You'll also develop transferable professional skills for a variety of fields that use programming.

Expand your understanding of mathematical analysis to cover continuity of functions, integration and differentiation. You will learn important topics in analysis of one variable and in several variables.

Discover how vector calculus and variational principles are applied to various areas of physics, such as classical mechanics, elements of electromagnetism and diffusion.

This non-credit preparatory module builds your employability skills. It provides knowledge and tools for placements, applications, recruitment processes, interviews, assessments, and career exploration.

Learn how to fit and interpret generalised linear models, and learn approaches to dealing with repeated measurement data. You'll develop an understanding of which models are likely to be appropriate in different situations.

Gain familiarity with the range of unsupervised and supervised methods used in statistical machine learning, and learn how these are used in research and industry. You'll have the opportunity to implement machine learning methods using statistical software and to interpret and communicate your findings.

Conduct a series of projects on mathematical or statistical topics and develop your technical and professional skills.

Learn how to solve problems using stochastic processes and concepts from a variety of applications, including molecular motion, population dynamics, weather and finances.

Learn how to develop and analyse a range of numerical methods for the solution of problems of continuous mathematics (including partial differential equations) and techniques in numerical linear algebra. You'll also learn how to implement methods using Python.

Explore a range of topics within number theory and cryptography, including the RSA cryptosystem and error correcting codes.

Learn about the analysis of dynamical systems and their application to real-world problems. You'll explore systems of linear and nonlinear differential equations, and understand how they're applied to biological issues such as population growth, cell systems, and the spread of infectious diseases.

Discover the basic ideas of asymptotic analysis and further develop your skills in solving a range of problems involving nonlinear equations, integrals and differential equations.

Continue your study of finite groups and apply your knowledge to the subject of Galois Theory, which examines the conditions under which polynomial equations can be solved using elementary algebraic operations.

Study the theory of integral equations, numerical methods for their solution, and their applications – including in meteorology. You will also study the theory of linear water waves, and examine a range of phenomena, including reflection, refraction, group velocity and shallow water waves.

Explore the core topics of mathematical physics, in particular Hamiltonian formulation of classical mechanics, quantum mechanics and statistical mechanics.

Gain theoretical and practical knowledge of image analysis and computer vision by using real-world applications. You'll develop professional skills such as problem solving, team-working, critical analysis, creativity and technical report writing for various audiences.

Learn methods to transform raw data into visual insights to develop your understanding of data integration. You'll work with peers to critically reflect on design process and outcomes, using commercial software tools and writing professionally for software design documents.

Study virtual reality in terms of scientific issues, application areas and strengths and weaknesses of the technology. You'll use software to design virtual worlds and/or games and learn techniques for their creation using computer graphics.

Study the field of text mining and natural language processing, focusing on the theories and practice of processing text data from the aspects of lexicons, syntactics and semantics.