This unit develops a student’s proficiency in working with mathematical methods, and it investigates some applications to problems in economics, management and related areas. The unit also develops the student’s understanding of the theoretical concepts behind these methods.

The course provides an introduction to business and management with particular emphasis on their international dimension.

Statistics 1 introduces students to the basic statistical concepts which they may need to understand and use in the other courses they intend to study in their degree. Statistics 2 requires the student to develop the concepts introduced in Statistics 1 of measurement and hypothesis testing.

In the last decade the demand for programming skills related to managing and visualizing data has grown remarkably. Python, R and SQL feature consistently in the top skills listed in data science and data analyst jobs. Knowing how to write efficient software code to handle and visualise data is an essential skill for any modern data scientist. This course will cover the main principles of computer programming with a focus on data science applications by following the entire pathway from raw data to databases, data wrangling and visualisation, machine learning frameworks up to software development.

People in business, economics and the social sciences are increasingly aware of the need to be able to handle a range of mathematical and statistical models. It must be admitted that many good managers are not very mathematically adept. However, they would be even more inquisitive, more precise, more accurate in their statements, more selective in their use of data, more critical of advice given to them etc. if they had a better grasp of quantitative subjects. Modelling is an important tool which all good managers should appreciate. The course extends and reinforces existing knowledge and introduces new areas of interest and applications of modelling in the ever-widening field of management.
Advanced Statistics: Distribution Theory: This half-course is intended for students who already have some grounding in statistics. It provides the basis for an advanced course in statistical inference.

Advanced Statistics: Statistical Inference: To infer means to make general statements on the basis of specific observations. From an early age, human beings are experts at inference. It is such a fundamental part of our intelligence that we do it without even thinking about it. We learn to classify objects on the basis of a very limited set of examples. In statistical inference, we go from specific to general via a mathematical model. Our specific observations come from a data set; that is, a collection of numbers, or at least, information that can be represented numerically. The mathematical models that we use draw on distributions of probability that are described in the companion half course ST2133 Advanced statistics: distribution theory. Methods for using probabilistic models to make general statements on the basis of an observed set of data is the central topic of this half course.

Econometrics is the application of statistical methods to the quantification and critical assessment of hypothetical economic relationships using data. This course gives students an opportunity to develop an understanding of econometrics to a standard that will equip them to understand and evaluate most applied analysis of cross-sectional data and to be able to undertake such analysis themselves.

For those undertaking market research in practice, an ability to handle data is an essential skill. This course concentrates on transforming students into competent and confident users of statistical software to enable them to conduct independent data analysis by taking a more applied approach to conventional statistics. The first half of the course focuses on aspects of market research, and in the second half the emphasis is on the practical application of a variety of multivariate statistical techniques to supplied datasets.

In the last decade there has been a remarkable growth in machine learning. Following recent advances in gathering, storing and managing vast amounts of observations, the ability to process high dimensional data and deal with uncertainty becomes increasingly important. Despite the increase of available information, inference may still lead to false conclusions in the absence of a suitable methodology. This course covers a wider range of such model based and algorithmic machine learning methods, illustrated in various real-world applications and datasets. At the same time, the theoretical foundation of the methodology is presented in some cases.

This course introduces the concept of money; what it is, why we use it and how it is created. It examines monetary policy in a closed economy, considering a number of models that allow real effects of monetary policy, ranging from new-Classical to Keynesian. Specific models will be introduced and solved, allowing students to see exactly how these models work and what differentiates one from another. It then studies Dynamic Stochastic General Equilibrium Models which brings together insights from Real Business Cycle Models and Keynesian macroeconomics. Finally, it studies uncertainty in monetary economics that is pervasive in macroeconomic modelling and takes the form of data, parameter and model uncertainty and introduces students to the concept of robust monetary policy design.

This course is about entrepreneurship both from an economics and practitioners angle and shows how economics can contribute to our understanding of entrepreneurship. The course also sheds light on the societal impact of entrepreneurship and its utility for economic development. It further provides practical elements such as developing business models, designing market research frameworks, product development cycles, and raising seed capital.