London School of Economics

Students are introduced to data science and its practice: how it works and how it can produce insights from social, political, and economic data. It combines accessible knowledge of data science as a field of study with practical knowledge about data science as a career path. By combining case studies in applications of both with the study of the content of data science, it covers data science that is both pedagogic but accessible, as well as fundamentally applied and practical. The course combines three perspectives: inferential thinking, computational thinking, and real-world relevance.

The course teaches quantitative techniques that guide evidence-based managerial decision-making. Students examine whether the predictions of managerial, social, or economic theory are supported by empirical evidence. Particular emphasis is on (a) the many ways in which evidence is abused in the academic or managerial debate, and (b) the causality in the relationship between variables. The approach is both formal, as the course makes extensive use of econometric theorems and techniques, and solidly grounded in intuition, as it provides numerous examples of tests of real-life relations. Many of these examples are illustrated using the STATA software package, and students learn the basics of data manipulation and regression techniques.

In this course students learn how to evaluate a brand strategy and how to use defined models and analytical tools to improve upon it. It covers the complete process, from consumer research, competitor analysis and positioning, to bringing the brand to life through design and activations. The course is based on the latest academic insights and infused with examples from our daily lives. It helps you prepare for a future as a marketeer, brand strategist, or entrepreneur.

This course examines, from a philosophical perspective, what is known about the minds of other animals - and what this means for the ethics of how people treat them.

This course teaches microeconomic analysis to let you explore important contemporary questions and special emphasis is given to the question how public policy can change (economic) outcomes.  Students will learn how to understand economic problems by focusing on their key characteristics, choosing the relevant microeconomic mechanisms and developing a solid intuition. The use of mathematics is minimal (in particular, with no calculus) and the emphasis of instruction is on graphical analysis and economic intuition. Precise topics and readings will be announced and are selected to be of current interest, such as the impact of the pandemic and environmental concerns.

This course offers an introduction to international macroeconomic theory and develops the main tools for macroeconomic policy analysis. Students study the balance of payments and the causes and consequences of global imbalances, followed by an in-depth study of the determination of exchange rates, money, and prices in open economies. They discuss the costs and benefits of different nominal exchange rate regimes and their sustainability, as well as examine the causes and consequences of debt and default, speculative attacks, and financial crises.

This course provides an introduction to Social Anthropology as the comparative study of human societies and cultures. Students are introduced to key themes and debates in the history of the discipline. Ethnographic case studies are drawn from work on a variety of societies, including hunter-gatherers, farmers, industrial laborers, and urban city-dwellers. Drawing on both classical and contemporary work, the course starts by posing the question: What is Social Anthropology? After exploring the ethnographic method and considering some historical background, the rest of the course is organized around core themes in the discipline, including (in the fall term) relatedness, exchange, and power. Through comparing different ethnographic examples, students consider key questions through anthropological perspectives. How do we become people and become related to others? What is love, and is it natural? Why do we think of some people as different and others as the same? Why are gifts and exchange so central to human societies? Does work empower or enslave us? What is power, and why do some people have it and others don’t?

The Napoleonic Empire was crucial in the formation of modern Europe. Much of Europe was covered by the Napoleonic Empire and its impact was felt across large parts of the non-European world. The influence of the emperor and his policies was most obvious in relation to the European international system, particularly through his military campaigns and his territorial reorganization of Europe in the wake of his successes. However, the Napoleonic era also saw major developments in the legal, constitutional, social, and economic order of many states, whether allied or opposed to the Napoleonic project. Likewise, in the aftermath of the French Revolution, much attention is paid to the impact of the Napoleonic era on the relationship between Church and State and the rise of national consciousness, whether in political or cultural terms. By studying how Napoleon's empire was created, challenged, and ultimately defeated, the course focuses on the nature of power and legitimacy in this era. An attempt is made to place the Napoleonic empire in a broader context, in part by comparing it to other contemporary, rival states, including Russia, Austria, and the United Kingdom. Finally, the course begins and ends with an assessment of the Napoleonic myth, both in terms of his contemporaries and for subsequent generations of historians.

Data science and machine learning are exciting new areas that combine scientific inquiry, statistical knowledge, substantive expertise, and computer programming. One of the main challenges for businesses and policy makers when using big data is to find people with the appropriate skills. Good data science requires experts that combine substantive knowledge with data analytical skills, which makes it a prime area for social scientists with an interest in quantitative methods. This course extends the foundation of probability and statistics with an introduction to the most important concepts in applied machine learning, with social science examples. It covers the main analytical methods from this field with hands-on applications using example datasets, so that students gain experience with and confidence in using the methods covered.