Start Date between Jan 24 - July 26

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.

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.

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.