Ireland

This course introduces students to the field of Human Geography, which is the study of the dynamic relations between people and places. Students gain an understanding of such complex processes as globalization and development, and the regional disparities in prosperity and inequality that result from these. The discussion evolves around the three main themes of economic, political, and social actions, all of which significantly shape the spatial organization of human activities. The course presents a general overview of the discipline, provides the opportunity to develop independent critical thinking skills, and offers insight into practical skills and tools that can be applied to a wide range of research settings. Overall, the course supplies the foundation for further, more topic specific, courses that focus on the spatial analysis of political and socio-economic phenomena at later stages.

In this course, students develop performance skills as set dancers. They learn to execute a set dance performance at the relevant level of competence and in an appropriate style; demonstrate specific set dancing styles; perform sympathetically within the context of a group; and critically understand the act of performance.

The course provides a thorough introduction to graph and network analysis from a computer science perspective. It covers the basic concepts and key algorithms in network analysis, and discusses their use in the context of many real-world applications across a variety of domains. Students learn to apply network analysis methods in practice through the medium of the Python programming language. Students taking this course must have previously completed the module COMP30760 "Data Science in Python". or an equivalent class at their home university.

Our increased longevity is one of the major achievements of modern humans, however this increase in lifespan does not necessarily mean an increase in health span – healthy, disease-free years. Students will explore some of the key challenges and opportunities associated with the expanding ageing population. They will use a multi-disciplinary approach (biological, clinical, societal) to explore several key questions such as: what happens the body during ageing that leaves us more susceptible to developing diseases such as cardiovascular disease, neurocognitive decline and cancer in later life? Why do some people age faster than others? How do we manage this challenge clinically? Can new models of care and novel technologies facilitate independent living in later life? What is it like for someone to get older in Ireland today? How can we ensure that everyone has the opportunity to age successfully in our society? What are the legal, ethical and economical challenges that we will face?

A sustainable future requires to focus not only on the responsible use of natural resources but also on the social, economic, and cultural challenges we face as a global society. This course explores the role education plays in key global issues such as poverty, migration, conflict, human rights abuses, and climate change to better understand how it can contribute to a sustainable and equitable society.

This course examines the collapse of communist system in East Central Europe and the post-1989 struggle for democracy in the region. The Polish case is examined closely as the example of this process. The course examines the process of transition from communism to democracy in East Central Europe and the global significance of the 1989 revolutions. It provides analysis of the core issues that shaped the region's politics: regime change, creation of civil society, economic reforms, and the changing nature of the post-communist system. 

This course develops appropriate methods and constructs enabling students to examine forces operating within food supply chains. The course covers supply chain analysis, vertical coordination, power and analytical frameworks. 

This course walks the students through the complex set of concepts and projects that form the Big Data stack. Students learn how to set up Big Data environments, how to use efficient data management operations and how to run algorithms - to the scale and speed required by Big Data datasets. At the end of the course, students design and implement their own solutions to address Big Data problems.