BSc (Hons) Mathematics Bachelor's degree at Strathclyde

This covers the basic concepts and standard methods of mathematical notation and proof; functions; complex numbers and variables; solution of equations; resolution of inequalities; sequences and series.

This introduces the fundamental concepts of calculus, and develops some of their applications including basic ordinary differential equations.

This gives an introductory treatment of vectors, matrices, geometry, and discrete maths and introduces their application to real-world problems.

A course that presents the place of mathematics and statistics in society, both historical and contemporary, while introducing some essential study skills, allowing students to develop and practice personal and technical skills.

A presentation of some basic ideas and techniques of statistics. This module will also introduce students to the R statistical software.

A module that focuses on communicating mathematics and statistics that will facilitate practical development of communication skills in data analysis, report writing and making presentations.

This module will introduce you to the basic ideas of linear algebra, such as matrices and determinants, vector spaces, bases, eigenvalues and eigenvectors. You'll study various standard methods for solving ordinary differential equations and understand their relevance.

This module will present basic ideas, techniques and results for calculus of two and three variables, along with differentiation and integration over curves, surfaces and volumes of both scalar and vector fields.

This module will give a rigorous treatment of convergence of sequences and infinite series of real numbers and of continuity, differentiability and integrability of functions of a real variable. It will illustrate the importance of these concepts in the analysis of problems arising in applications.

This module will present the basic concepts of probability theory and statistical inference and provide you with the tools to appropriately analyse a given data set and effectively communicate the results of such analysis.

This module will develop your understanding and application of mathematical modelling techniques in a wide variety of contexts. This will include tools including vectors and ordinary differential equations, applied in a wide variety of contexts.

This module will introduce you to the R computing environment. It'll enable you to use R to import data and perform statistical tests, allow you to understand the concept of an algorithm and what makes a good algorithm and will equip you for implementing simple algorithms in R.

This module will introduce functions of a complex variable, define concepts such as continuity, differentiability, analyticity, line integration, singular points, etc. You will examine some important properties of such functions and consider some applications of them, for example, conformal mappings and the evaluation of real integrals using the Residue Theorem. You will also be introduced to Fourier and Laplace transform methods for solving linear ordinary differential equations and convolution

In this module we'll introduce basic algebraic structures, with particular emphasis on those pertaining to finite dimensional linear spaces and deepen your understanding of linear mappings. We'll also provide an introduction to inner product spaces and bilinear forms.

In this module we'll introduce you to analytical methods for solving ordinary and partial differential equations, so you'll develop an understanding along with technical skills in this area.

In this module you will be introduced to the basic theory and applications of: metric spaces normed vector spaces and Banach spaces inner product spaces and Hilbert spaces bounded linear operators on normed linear spaces

This module will: review the concepts of probability distributions and how to work with these present approaches to parameter estimation, focusing on maximum likelihood estimation, bootstrap estimation, and properties of estimators present hypothesis testing procedures, including classical likelihood ratio tests and computer-based methods for testing parameter values, and goodness-of-fit tests introduce and provide understanding of the least squares multiple regression model, general linear mode

This module will: convey the generalisation of the mechanics of single-particle systems to many-particle systems convey the central ideas of a continuum description of material behaviour and to understand relevant constraints ground students in the basic principles governing three-dimensional motions of rigid bodies convey how the ideas of continuum theory are applied to static and inviscid fluids

This module will motivate the need for numerical algorithms to approximate the solution of problems that can't be solved with pen and paper. You'll develop your skills in performing detailed analysis of the performance of numerical methods and will continue to develop your skills in the implementation of numerical algorithms using R.

You'll be introduced to the basic concepts of random phenomena evolving in time, from two complementary points of view: probabilistic modelling and data-driven analysis. Presentation of underlying ideas of simple stochastic processes, time series models, and the associated probability theory and statistical techniques will be covered. In addition to applications of the methods to financial and economic systems, including modelling, data analysis, and forecasting.

This module provides you with experience of the skills required to undertake project work, and to communicate the findings in written and oral form using a variety of sources, such as books, journals and the internet. You will undertake an individual research project, researching a mathematical or statistical topic and writing a short report on it.

In this module you'll get an introduction to ideas in mathematics and statistics that can be used to model real systems, with an emphasis on the valuation of financial derivatives. This module places equal emphasis on deterministic analysis (calculus, differential equations) and stochastic analysis (Brownian motion, birth and death processes). In both cases, in addition to theoretical analysis, appropriate computational algorithms are introduced. The first half of the module introduces general m

This module will present the main results in Functional Analysis. You will also be introduced to linear operators on Banach and Hilbert spaces and study applications to integral and differential equations.

You will be provided with a range of applied statistical techniques that can be used in professional life. This module provides you with the fundamental principles of statistical modelling through experimental design and multivariate analysis.

In this module you'll be introduced to the theory of Newtonian fluids and its application to flow problems and the dynamics of waves on water and in other contexts.

In this module you'll be presented with the basic theory and practice of finite element methods and polynomial and piecewise polynomial approximation theory.

In this module you'll be introduced to a range of modern statistical methods and practices used in industry, commerce and research, and you will develop skills in your application and presentation.