University of Edinburgh

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.

This course introduces students to the principal findings, models, and research methods in the field of second language acquisition. The course surveys general issues such as the role of the native language, the effects on the second language on the first, universals, age, input and interaction, and processing, as well as characteristics of the acquisition of phonology, lexicon, and syntax in second language learners. The empirical component of the course provides students with experience in designing and carrying out studies in second language acquisition.

In this course, students are exposed to contemporary research on variation in a diverse range of languages, and are expected to engage with research covering some of the following topics: complex linguistic data from a range of languages (not solely English); diachronic processes of change and the social factors involved in them; patterns of synchronic, inter-dialectal variation in specific present-day languages; language-internal and language-external factors affecting variation; sound change and phonetic variation; patterns of variation and change affecting morphosyntax; and empirical methodologies including experimental research and statistical analysis techniques.

This course provides an understanding of the key determinants of economic development, inequality, and trade. It combines economic theories with empirical studies. It is divided into two halves. In the first half, students study how the economic development of different regions is interconnected through trade. Students begin by examining the patterns of international trade. They then define and use the principle of comparative advantage. They formalize the reasons why countries trade using classical theories and general equilibrium models of trade. These models highlight that trade can generate both gains and inequalities. In the second half, students begin by defining economic development and measuring the gap between poor and rich countries. They define and make use of concepts like poverty, inequality, and economic growth. They then review the classic theories of economic growth, which attempt to explain why some countries are rich and others are poor and contrast those with the contemporary models of development. Students also consider the role of political institutions and human capital in generating economic development.

This introductory course in computer graphics comprises of three parts. The first part of the course presents a bird's-eye view of the current state-of-the-art in the field. The latter two parts cover rendering, which is one of the core topics in computer graphics, in detail. The second part of the course teaches central concepts in rendering, along with the relevant mathematics. Finally, the third part of the course focusses on applications of the theory taught in the second part.