This module is tailored towards students who are combining mathematics with scientific study. Emphasis is on topics and techniques that are needed for...

Linear Algebra and Vector Calculus (NSCI0027)

This module is tailored towards students who are combining mathematics with scientific study. Emphasis is on topics and techniques that are needed for applications such as bioinformatics, electromagnetism, fluid dynamics, modelling of epidemics, and turbulence.

Students will study partial differentiation and multivariable calculus vector calculus, multidimensional integration, matrices, vector spaces and linear algebra techniques.

This module provides an accessible introduction to some of the key mathematical, statistical, and computational ideas and techniques that are used in...

Mathematics for Natural Sciences B (NSCI0006)

This module provides an accessible introduction to some of the key mathematical, statistical, and computational ideas and techniques that are used in scientific research today.

The module can be taken by students with or without a mathematics qualification equivalent to A-level.

 

You will learn to :

[1] Apply descriptive and inferential statistics to draw scientific conclusions

[2] Explore dynamical systems using examples drawn from a range of scientific areas

[3] Gain insight using a combination of analytic and computational approaches

[4] Communicate mathematical and statistical content accurately

 

 

The topics that you will study are:

Statistical inference: An introduction to the computer language of R. Description and examination of datasets. Hypothesis testing.

Observing and recording: Definition of a function. Graphical representation of functions and informal notion of a limit. Link between mathematical functions and observed behaviours such as oscillations, growth and decay.

Rates of change: The concept of the derivative and results for some standard derivatives. Differential equations and their meaning. Construction of a model.

Computer simulation: Numerical solution of differential equations. Visualising and interpreting system dynamics. Recognising and understanding common patterns of behaviour such as oscillations, growth and decay.

Analytic vs computer models: Similarities and differences between analytic models and calculator/computer models. Concept of integration and elementary examples