You will receive practical and theoretical training in two core modules and further specialisation as well as broader knowledge in six optional modules of your choice. Teaching at Master’s level places greater emphasis on practical work than at undergraduate level. This includes coursework reports and learning state-of-the-art specialised research subfields.
After completing successfully the taught portion of the course, you will continue for a further ten weeks to put your knowledge into practice through a dissertation.
Statistical Methods
The module content will include a thorough grounding in classical and Bayesian methods of statistical inference with an introduction to selected modern developments in statistical methodology. Since MSc students have different background knowledge in statistics, we start afresh although a solid mathematical background is assumed. At the end of the course you will have a solid background in basic concepts of statistical methodology and knowledge at an advanced level in some areas.
An Introduction to Statistical Practice
Introduction to Statistical Practice module introduces statistical computing, using R, through hands-on practical classes on the analysis of real data from a variety of scientific and other disciplines; and develops such skills are report-writing, statistical graphics, etc.
Dissertation
To complete the MSc, a student also undertakes a substantial project under the supervision of a Department member, and writes a dissertation reporting the results. Such projects can be in any of the areas covered by the MSc, including applied statistics, statistical methodology, computational methods, probability etc.
Optional modules
The remaining six modules are chosen from a wide range of options, subject to availability, to suit the interests of individual students. The options include:
Advanced Topics in Data Science
This module will be comprised of three selected topics in the area of computational challenges associated with data analysis. The topics may change year to year. Some examples of topics from previous academic years: Deep Learning for Natural Language Processing, Decision Trees and Random Forests, Model Comparison and Selection, Artificial Neural Networks, Introduction to Reinforcement Learning and Modelling the Written Word: Compression and Human-Computer-Interfaces.
Bayesian Forecasting and Intervention with Advanced Topics
Forecasting is a vital prerequisite to decision making. This course offers a very powerful fundamental probabilistic approach to forecasting, controlling and learning about uncertain commercial, financial, economic, production, environmental and medical dynamic systems. The theory will be illustrated by real examples from industry, marketing, finance, government, agriculture etc. A familiarity with the material in this module will be very useful to all students planning a career involving a component of industrial, business or government statistics.
Applied Stochastic Processes with Advanced Topics
This module provides an introduction to concepts and techniques which are fundamental in modern applied probability theory and operations research: Models for queues, point processes, and epidemics. Furthermore, we study notions of equilibrium, threshold behaviour, and description of structure. The ideas presented in this module have a vast range of applications, for example routing algorithms in telecommunications (queues), assessment of apparent spatial order in astronomical data (stochastic geometry), description of outbreaks of disease (epidemics).
Medical Statistics with Advanced Topics
Modern applications of statistics to medicine are highly developed, and many medical research papers employ statistical techniques. Large numbers of statisticians are employed in medical research establishments, particularly in pharmaceutical companies and medical schools. Medical statistics continues to be a buoyant area for statistical recruitment. The course will explain why and how statistics is used in medicine, and study some of the statistical methods commonly used in medical research. We will include examples from our own research. The statistical techniques applied to medical data are also relevant in other applications.
Monte Carlo Methods
When modelling real world phenomena statisticians are often confronted with the following dilemma: should we choose a standard model that is easy to compute with or use a more realistic model that is not amenable to analytic computations such as determining means and p-values. We are faced with such choice in a vast variety of application areas, some of which we will encounter in this module. These include financial models, genetics, polymer simulation, target tracking, statistical image analysis and missing data problems. With the advent of modern computer technology we are no longer restricted to standard models as we can use simulation-based inference.
Designed Experiments with Advanced Topics
Designed experiments are used in industry, agriculture, medicine and many other areas of activity to test hypotheses, to learn about processes and to predict future responses. The primary purpose of experimentation is to determine the relationship between a response variable and the settings of a number of experimental variables (or factors) that are presumed to affect it. Experimental design is the discipline of determining the number and order (spatial or temporal) of experimental runs, and the setting of the experimental variables.
Multivariate Statistics with Advanced Topics
Multivariate data arises whenever several interdependent variables are measured simultaneously. Such high-dimensional data is becoming the rule, rather than the exception in many areas: in medicine, in the social and environmental sciences and in economics. The analysis of such multidimensional data often presents an exciting challenge that requires new statistical techniques which are usually implemented using computer packages. This module aims to give you a good and rigorous understanding of the geometric and algebraic ideas that these techniques are based on, before giving you a chance to try them out on some real data sets.
Statistical Frontiers
This module is also taken by PhD students in the Warwick Centre for Doctoral Training in Mathematics and Statistics. Each topic will be presented by a different lecturer, who is an expert in the research area. The lectures are intended to introduce a particular research topic, provide a short overview and stimulate your interest in this particular area. Taken as a whole, the module gives a (necessarily partial and incomplete) idea of the breadth of the research interests and expertise within the Department and should thus help you discover the broadness of research areas in statistics and probability that are at the cutting edge of interest.
