University of Edinburgh

Celtic languages are presently spoken in Scotland, Wales, Ireland, on the Isle of Man, in Cornwall and in Brittany, as well as in a small number of diasporic communities. This course explores the emergence of these Celtic speech communities into the historical record in the Middle Ages, the social, political and cultural forces which have shaped their development, and their current prospects for survival. The impact of the development of central state authorities, the protestant Reformation, wider British and French politics, the Enlightenment, Romanticism, the emergence of the modern nation-state, contemporary minority discourses will be considered. Literary and other sources in the various Celtic languages (in translation) will be used to explore these themes. While the focus will be sociolinguistic and literary, linguistic characteristics of the languages will be referred to from time to time.

This course complements PRINCIPLES OF FINANCE, which is a theoretical and conceptual course that introduces core ideas of finance. APPLICATIONS OF FINANCE focuses on empirical and practical applications of these concepts and theories. The course introduces students to some of the practical aspects of finance, for example, valuation, trading strategies based on derivatives, and new finance applications, such as blockchain and high-frequency trading.

This course provides an introduction to the scientific basis of modern medicine and the role of biological sciences in the understanding and treatment of disease. This is delivered through a series of lectures, facilitated group discussions, a practical class and assessed written reports. The course provides a basic understanding of practical material relevant to biological sciences and enables students to develop personal skills in interpreting basic scientific research and communicating scientific ideas and information in a clear, accurate, and well-organized manner.

The course familiarizes students with the issues involved in designing, implementing, and applying parallel programming systems. Initial motivation is provided by consideration of a number of typical high performance applications and parallel architectures. This highlights the role of parallel software systems as a means of bridging the gap between these and allows abstraction of the issues which must be addressed by any such system (partitioning, communication, agglomeration, scheduling). It explores the ways in which these challenges have been addressed by a range of systems, including both de facto standards and more adventurous research projects.

The course has a practical focus and introduces students to a range of basic and more advanced network analysis methods through hands-on computer work. Through lectures and readings, students learn key concepts and measures of social network research. In labs, students apply this knowledge through exercises with real-world network datasets using the statistical environment R. The course first covers exploratory Social Network Analysis (SNA) before progressing into more advanced statistical methods.

The course provides students with an understanding of contemporary societal and policy debates around key energy policy challenges in the context of the transition towards sustainable and lower carbon energy systems. The course will take a distinctive Science, Technology, and Innovation Studies (STIS) approach which equips students with the analytical tools necessary to critically evaluate key energy technology and policy debates in the UK, Europe, and globally.

The course examines music's various roles in society and the effects of the various ways in which societies are organized on the ways in which music itself is made, heard, and understood. It introduces students to the sociology and psychology of music and encourages them to think conceptually about their own musical activities. The course covers a wide range of musical practices - Western and non-Western, classical and popular, past and present - though it focuses on musical and social developments since the Industrial Revolution.

This course provides an introduction to programming within the statistical package R. Various computer-intensive statistical algorithms are discussed and their implementation in R is investigated. Topics to include basic commands of R (including plotting graphics); data structures and data manipulation; writing functions and scripts; optimizing functions in R; and programming statistical techniques and interpreting the results (including bootstrap algorithms).

This course focuses on supply chain management topics of operations management. Its goal is to help students become effective managers in today's competitive, global environment. Students gain an understanding of what supply chain managers do, and that supply chain management is a highly complex activity and involves many business functions.

The course provides engineering students with the skills to process and examine different forms of data in Python, and an understanding of how machine learning methods can use this data to solve classification and regression problems. Students learn how to implement these methods in Python using Scikit-learn. Students gain an awareness of when it is appropriate to use a particular method (if any), best practices, and the ethical issues that can occur when sourcing data and deploying machine learning in the real world.