MSc Applied Statistics (online)

The course and thus this introductory class is aimed at graduates who have not previously studied statistics at university level. The class will provide the foundation elements of probability and statistics that are required for the more advanced classes studied later on.

This class will introduce the R computing environment and enable you to import data and perform statistical tests. The class will then focus on the understanding of the least squares multiple regression model, general linear model, transformations and variable selection procedures.

This class will cover the fundamental statistical methods necessary for the design and analysis of scientific experiments. There will be an emphasis on the use of real data and the interpretation of statistical analyses in the context of the research hypothesis under investigation.

Surveys are an important way of collecting data. This class will introduce you to the methods that are commonly used to design questionnaires and analyse data resulting from these questionnaires.

This class will introduce you to Bayesian statistics and the modern Bayesian methods that are used in a variety applications. Like with other classes, the focus is on real-life data and using statistical software packages for analysis.

This class covers all aspects of statistical consultancy skills necessary for being a successful statistician working in any research or customer environment. You will work on real-life problems in small groups and have the opportunity to interact with stakeholders researchers to formulate hypotheses.

You'll be exposed to a number of diverse topics in econometrics that can be used to model real financial data, with an emphasis on the analysis of financial time series. The statistical software R is introduced for financial modelling.

The class aims to expose you to a number of diverse topics in stochastic processes that can be used to model real systems, with an emphasis on the valuation of financial derivatives. In additional to theoretical analysis, appropriate computational algorithms using R are introduced.

This class will cover the fundamental statistical methods necessary for the application of classical statistical methods to data collected for health care research. There will be an emphasis on the use of real data and the interpretation of statistical analyses in the context of the research hypothesis under investigation. Software packages such as Minitab will be introduced.

The aim of this class is to equip you with a sound understanding of the principles of machine learning and a range of popular approaches, along with the knowledge of how and when to apply the techniques. The class balances a solid theoretical knowledge of the techniques with practical application via Python (and associated libraries) and students are expected to be familiar with the language. Aspects of the course will be highly mathematical and technical requiring strong math and programming ability (Python and Tensorflow).