Start Date between Jan 24 - July 26

The module provides a general introduction to statistical thinking and data analysis including probability rules and distributions, methods of estimation and hypotheses testing and present the basics of Bayesian inference.

This module introduces the fundamental skills of programming in python. The aim is for students to develop the skills and experience to independently translate a broad range of data science related problems into functioning computer programs and communicate the results.

This module introduces the fundamental skills of programming in python. The aim is for students to develop the skills and experience to independently translate a broad range of data science related problems into functioning computer programs and communicate the results.

This module develops the skills necessary to be a successful data scientist that can deliver projects to a professional standard, as would be expected by an employer or client.

This module develops the skills necessary to be a successful data scientist that can deliver projects to a professional standard, as would be expected by an employer or client.

This module gives students the opportunity to demonstrate the skills necessary to design and deliver a short project in data science in an area such as AI, health informatics, urban analytics, statistical and mathematical methods, visualisation and immersive technologies.

This module gives students the opportunity to demonstrate the skills necessary to design and deliver a short project in data science in an area such as AI, health informatics, urban analytics, statistical and mathematical methods, visualisation and immersive technologies.

The module provides a general introduction to statistical thinking and data analysis including probability rules and distributions, methods of estimation and hypotheses testing and present the basics of Bayesian inference.

This course will introduce students to basic techniques, which can be used to perform a preliminary investigation of data sets. Exploring data involves visualising the variables and relationships to help determine outliers, identify trends, suggest suitable statistical models and inform future data gathering.

This course will introduce students to basic techniques, which can be used to perform a preliminary investigation of data sets. Exploring data involves visualising the variables and relationships to help determine outliers, identify trends, suggest suitable statistical models and inform future data gathering.

Machine learning is a rapidly developing research area which takes an algorithmic approach to identifying patterns and statistical regularities in data without or with limited human intervention, often with the aim of supporting decision making. In this module you will learn to apply a number of machine learning techniques that are widely used in industry, government, and other large organisations. You will learn how the different approaches relate to and are motivated by statistics and will gain practical experience in the application of these techniques on real and simulated datasets.

Machine learning is a rapidly developing research area which takes an algorithmic approach to identifying patterns and statistical regularities in data without or with limited human intervention, often with the aim of supporting decision making. In this module you will learn to apply a number of machine learning techniques that are widely used in industry, government, and other large organisations. You will learn how the different approaches relate to and are motivated by statistics and will gain practical experience in the application of these techniques on real and simulated datasets.

In many areas of science and social study, several variables or measurements are taken from each member of a sample, with one variable regarded as an ‘outcome’ and the others regarded as ‘predictors’ of the outcome. This module will examine ways of predicting one particular variable from the remaining measurements using the linear regression model. The general theory of linear regression models will be covered, including variable selection, tests and diagnostics and methods to deal with outliers. While linear regression is a tremendously useful statistical method, it has limitations. Generalised linear models extend linear regression in many ways - allowing us to analyse more complex data sets. In this module we will see how to combine continuous and categorical predictors, analyse binomial response data and model count data.?

In many areas of science and social study, several variables or measurements are taken from each member of a sample, with one variable regarded as an ‘outcome’ and the others regarded as ‘predictors’ of the outcome. This module will examine ways of predicting one particular variable from the remaining measurements using the linear regression model. The general theory of linear regression models will be covered, including variable selection, tests and diagnostics and methods to deal with outliers. While linear regression is a tremendously useful statistical method, it has limitations. Generalised linear models extend linear regression in many ways - allowing us to analyse more complex data sets. In this module we will see how to combine continuous and categorical predictors, analyse binomial response data and model count data.?

The use of computers in mathematics and statistics provides a wide range of techniques for studying otherwise intractable problems and for analysing very large data sets. "Statistical computing" is the branch of mathematics which concerns these techniques for situations which either directly involve randomness, or where randomness is used as part of a mathematical model. This module gives an overview of key methods in statistical computing. One of the most important ideas in statistical computing is, that often properties of a stochastic model can be found experimentally, by using a computer to generate many random instances of the model, and then statistically analysing the resulting sample. The resulting methods are called Monte Carlo methods, and discussion of such methods forms the main focus of this module.

The use of computers in mathematics and statistics provides a wide range of techniques for studying otherwise intractable problems and for analysing very large data sets. "Statistical computing" is the branch of mathematics which concerns these techniques for situations which either directly involve randomness, or where randomness is used as part of a mathematical model. This module gives an overview of key methods in statistical computing. One of the most important ideas in statistical computing is, that often properties of a stochastic model can be found experimentally, by using a computer to generate many random instances of the model, and then statistically analysing the resulting sample. The resulting methods are called Monte Carlo methods, and discussion of such methods forms the main focus of this module.

The objective of this module is to introduce Bayesian statistical methods through the consideration of philosophical differences with traditional statistical procedures and the application of Bayesian techniques. This module also introduces the ideas of quantitative decision theory and rational decision making.

The objective of this module is to introduce Bayesian statistical methods through the consideration of philosophical differences with traditional statistical procedures and the application of Bayesian techniques. This module also introduces the ideas of quantitative decision theory and rational decision making.