This module aims to provide students with a deep understanding of sport and exercise performance at an elite level, including testing and training methods...

Sport &amp; Exercise Performance Optimisation  (SURG0188)

This module aims to provide students with a deep understanding of sport and exercise performance at an elite level, including testing and training methods aimed at optimising performance in elite athletes and translational aspects of athletic performance to wider populations.

The content of the module gives a comprehensive overview of the most accurate and efficient evidence-based methods for performance optimisation, ranging from exercise testing, sport analysis, training strategies, injury prevention, ergogenic aids and coaching psychology.

Upon completion of the module, the student will be able to design a performance optimisation plan for an elite athlete with consideration of the multidisciplinary domains that influence performance. In addition to this, the student will also be able to translate the concepts of sport performance optimisation to the general and clinical populations, bridging the gap between elite performance and population health.

Aims - This module aims to provide a general background on fundamental statistical methods and applications in data science. It is intended for students...

Introduction to Statistical Data Science (STAT0032)

Aims - This module aims to provide a general background on fundamental statistical methods and applications in data science. It is intended for students registered on certain taught postgraduate degree programmes offered by the Department of Statistical Science, or jointly with other departments.

Objectives - On successful completion of the module, students should have an understanding of the fundamental aspects of probability and statistics sufficient to follow other taught postgraduate level modules in Statistical Science. Students should also be equipped to lead basic data analysis projects in industry and research. The module will teach students: how to use probability as a language to express uncertainty, ways of visualizing and preparing data for statistical analysis, estimation techniques in the context of applied data analysis problems, the role of algorithms in the computation of estimators, how to express uncertainty in estimation via confidence intervals and hypothesis testing, predictive analysis from the point of view of regression.

Applications - The statistical methods introduced in STAT0032 are very general, and they are used in almost all areas in which statistics is applied. In the module, we will discuss applications in the context of business, social sciences, and biology, among others.

Content - Exploratory data analysis: basic visualisation for data preparation and modelling strategy. Review of probability models, in the context of the different statistical methods discussed in the module. Hypothesis testing and confidence intervals: methods for assessing the uncertainty in the analysis. Regression: linear and non-linear methods for explaining outcomes. Point estimation, maximum likelihood and basic optimization: fitting generic statistical models. Dimensionality reduction: explaining the variability in datasets using fewer dimensions.

Key texts - Available from ReadingLists@UCL.