Computational Statistics|Data Collection and Processing|Information and Data Science|Quantitative Analysis|Statistics

This Master&#039;s level module introduces students to a range of statistical and mathematical tools for analysing and interpreting data. The module also...

This Master's level module introduces students to a range of statistical and mathematical tools for analysing and interpreting data. The module also focuses on key skills, such as communicating data, writing technical reports, and approaching quantitative problems. Applications and examples concentrate on the field of cities research. Explanations are intended to develop conceptual understanding rather than technical mathematical frameworks. Little to no prior knowledge is assumed. This module is of most relevance to students from the social sciences or the field of cities research specifically who wish to develop their quantitative skills. It is not appropriate for students from outside the Centre for Advanced Spatial Analysis who already have significant technical training (e.g. a background in mathematics or the natural sciences). Python programming will be used in this module. If students do not have experience in Python, they are strongly recommended to take CASA0013 in Term 1. Content covered includes: Fermi Estimates, Linear Regression, Hypothesis Testing, Clustering, Linear Programming, Statistical Fallacies, Systems Dynamics Models. 

Description

Undergraduate 

Module Content

Course will introduce Medical Anthropology, which is the subfield of anthropology concerned with how human...

Aims - This module aims to provide an introduction to the ideas underlying the calculation of risk from a Bayesian and frequentist standpoint, and the...

Aims - This module aims to provide an introduction to the ideas underlying the calculation of risk from a Bayesian and frequentist standpoint, and the structure of rational, consistent decision making. It is intended for taught postgraduate students registered on the degree programmes offered by the UCL Institute for Risk and Disaster Reduction.

Objectives - On successful completion of the module, a student should be able to understand and compute financial measures of risk, understand the concepts of decision theory, find appropriate probability models for risky events and check the validity of the underlying assumptions, and be familiar with methodology for detecting changes in risk levels over time.

Applications - The ideas introduced in this module provide a generic framework for thinking about risk and decision-making in the presence of uncertainty. As such, they can be applied in many diverse areas. The module will include examples from natural hazards and finance using the R statistical software package.

Content - Introduction to Bayesian inference: conditional probability, Bayes' theorem, elicitation of subjective probabilities. Statistical decision theory: expected loss, randomised and non-randomised decision rules, decision principles, comparison of decision rules. Measures of risk: Value-at-Risk, Expected Shortfall. Dependence modelling: copula theory and its applications. Extreme value theory: probability models for the occurrence of extreme events, block maxima, peaks-over-threshold. Time series approaches to modelling volatility and detecting/modelling change in risk over time: ARMA models, ARCH/GARCH models.

Key texts - Available from ReadingLists@UCL.