*Please note this course is subject to review.
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This module aims to give students a solid grounding in some of the most important analysis methods. It looks at the different practices and assumptions made in different applied scientific disciplines. It provides students with an understanding of the empirical techniques commonly used in statistical analysis as well as the ability to use these techniques and critically evaluate and interpret empirical work.
This module aims to give students a solid grounding in some of the most important methods employed by statisticians by providing a deeper understanding of probability theory and random processes. Students will be introduced to modern topics and techniques in stochastic processes. They will learn the relevant theory and gain the ability to formulate and solve practical problems.
This module aims to introduce students to advanced techniques in inference theory. It develops students’ ability to understand statistical theory as well as applying it to computational methods. Students are introduced to a wide-range of advanced techniques in classical inference and are given a practical introduction to Bayesian analysis.
On this module, students are taught the important concepts of descriptive statistical analysis applied on different types of data sets. The course will develop students’ appreciation of the task of a statistician for critically analysing data sets and will be useful to anyone considering a job in statistics. Students will develop a keener understanding about structures that underlie data observations.
Data obtained from observations collected sequentially over time are extremely common. The purpose of time series analysis is to understand or model the stochastic mechanism that gives rise to an observed series and to predict or forecast the future values of a series based on the history of that series, and, possibly, other related series or factors.
The quantity of data available to analysts is growing at an ever-increasing rate. This data has become a vital tool for decision-making in a competitive world. However, the size, which makes the data so valuable, also makes it difficult to analyse using traditional statistical methods. This module introduces the student to a variety of methodologies now employed to explore, analyse, categorise and visualise data from large data sets.
This module aims to introduce statistical methods used for modelling and evaluating survival data as well as to implement estimation and test procedures. Survival models are used in bio-statistical, epidemiological and health related fields, as well as in research in the physical sciences including economic, financial, sociological, psychological, political and anthropological data.
You can find more information about this course in the programme specification. Module and programme information is indicative and may be subject to change.
We’ll carefully manage any future changes to courses, or the support and other services available to you, if these are necessary because of things like changes to government health and safety advice, or any changes to the law.
Any decisions will be taken in line with both external advice and the University’s Regulations which include information on this.
Our priority will always be to maintain academic standards and quality so that your learning outcomes are not affected by any adjustments that we may have to make.
At all times we’ll aim to keep you well informed of how we may need to respond to changing circumstances, and about support that we’ll provide to you.
Start: October 2020, EU/INT induction: September 2020
Duration: 1 year full-time, 2 years part-time