The number of composite indices that are constructed and used internationally is growing very fast; but whilst the complexity of quantitative techniques has increased dramatically, the education and training in this area has been dragging and lagging behind. As a consequence, these simple numbers, expected to synthesize quite complex issues, are often presented to the public and used in the political debate without proper emphasis on their intrinsic limitations and correct interpretations. In this course on global statistics, offered by the University of Geneva jointly with the ETH Zürich KOF, you will learn the general approach of constructing composite indices and some of resulting problems. We will discuss the technical properties, the internal structure (like aggregation, weighting, stability of time series), the primary data used and the variable selection methods. These concepts will be illustrated using a sample of the most popular composite indices. We will try to address not only statistical questions but also focus on the distinction between policy-, media- and paradigm-driven indicators.

By the end of this project, you will learn about the concept of document scaling in textual analysis in R. You will know how to load and pre-process a data set of text documents by converting the data set into a corpus and document feature matrix. You will know how to run an unsupervised document scaling model and explore and plot the scaling outcome.

Welcome to Necessary Condition Analysis (NCA). NCA analyzes data using necessity logic. A necessary condition implies that if the condition is not in place, there will be guaranteed failure of the outcome. The opposite however is not true; if the condition is in place, success of the outcome is not guaranteed. Examples of necessary conditions are a student’s GMAT score for admission to a PhD program; a student will not be admitted to a PhD program when his GMAT score is too low. Intelligence for creativity, as creativity will not exist without intelligence, and management commitment for organizational change, as organizational change will not occur without management commitment. NCA can be used with existing or new data sets and can give novel insights for theory and practice. You can apply NCA as a stand-alone approach, or as part of a multi-method approach complementing multiple linear regression (MLR), structural equation modelling (SEM) or Qualitative Comparative Analysis (QCA). This course explains the basic elements of NCA and uses illustrative examples on how to perform NCA with R software. Topics include (i) Setting up an NCA study (ii) Run NCA and (iii) Present the results of NCA. We hope you enjoy the course!

This course examines the relation of advertising to society, culture, history, and the economy. Using contemporary theories about visual communications, we learn to analyze the complex levels of meaning in both print advertisements and television commercials. About the Course The course covers a wide range of topics, including the origins of advertising, the creation of ads, the interpretation of ads, the depiction of race, class, gender, and sexuality in advertising, sex and selling, adverting and ethics, and the future of advertising. The lectures will discuss theoretical frameworks and apply them to specific advertisements. Course Syllabus Week 1: What is advertising and where did it come from? Week 2: Am I being manipulated by advertising? Week 3: What’s in an ad beyond that which meets the eye? Week 4: How do ads get made? Week 5: What do ads teach us about race, class, gender, and sexuality? Week 6: Does sex sell? Week 7: What is the future of advertising? Recommended Background No background is required; everyone is welcome! Suggested Readings Although the lectures are designed to be self-contained, we recommend that students refer to the free online textbook ADTextOnline.org. Other free resources will be suggested for each week’s module. Course Format Most videos will be lectures with instructor talking. Each lecture will be illustrated with PowerPoint slides, print advertisements, and TV commercials. The videos for each week will consist of segments that add up to about an hour. Each week will have one quiz that will appear as stand-alone homework. All resources beyond lectures will be available online to students at no charge. Most of these will be from ADTextOnline.org. Others will be visits to the sites of ad agencies in the US and abroad, open access websites that deal with course topics, and open-access journal articles.

Introduction to Financial Engineering and Risk Management course belongs to the Financial Engineering and Risk Management Specialization and it provides a fundamental introduction to fixed income securities, derivatives and the respective pricing models. The first module gives an overview of the prerequisite concepts and rules in probability and optimization. This will prepare learners with the mathematical fundamentals for the course. The second module includes concepts around fixed income securities and their derivative instruments. We will introduce present value (PV) computation on fixed income securities in an arbitrage free setting, followed by a brief discussion on term structure of interest rates. In the third module, learners will engage with swaps and options, and price them using the 1-period Binomial Model. The final module focuses on option pricing in a multi-period setting, using the Binomial and the Black-Scholes Models. Subsequently, the multi-period Binomial Model will be illustrated using American Options, Futures, Forwards and assets with dividends.

The course introduces you to cutting-edge research in the economics of AI and the implications for economic growth and labor markets. We start by analyzing the nature of intelligence and information theory. Then we connect our analysis to modeling production and technological change in economics, and how these processes are affected by AI. Next we turn to how technological change drives aggregate economic growth, covering a range of scenarios including a potential growth singularity. We also study the impact of AI-driven technological change on labor markets and workers, evaluating to what extent fears about technological unemployment are well-founded. We continue with an analysis of economic policies to deal with advanced AI. Finally, we evaluate the potential for transformative progress in AI to lead to significant disruptions and study the problem of how humans can control highly intelligent AI algorithms.

After this course you will be an Earth Economist that can provide evidence-based advise on the best global policy. As an Earth Economist you will better understand the behavior and advice of economists, have become a better economist yourself and know where to find Earth's data and how to analyze these world observations. Our planet is too important: we need you to get engaged! Earth Economics offers a completely new angle to policy analysis by its focus on the truly global level and its empirical orientation on very recent data. Sustainability (environmental and related to the UN's SDGs), equality and heterodox (that is: non mainstream) views on the economy are important for an Earth Economist. Taking stock of emerging planet data and analyzing policies during and following the Global Crisis, Earth Economics provides both a topical introduction into basic economic tools and concepts as well as insights in highly relevant problems and recent developments in planet production, growth and governance. An important issue is the provision of global public goods. Earth Economics highlights the importance of the United Nations, International Monetary Fund, the World Health Organization and the World Trade Organization.

Arctic communities have diverse histories and roles in local, regional and global economies. However, the scope and scale of globalization has increased so quickly that vulnerable Arctic communities are facing new kinds of challenges to their survival. In this 3-week MOOC, a unique collaboration between the University of Alberta and UiT The Arctic University of Norway, you will investigate the challenges faced by Indigenous, North American, Russian and Nordic Arctic communities in a modern world. So join us as we venture above the 60th parallel North, and explore how these fascinating communities adjust to change while maintaining their ways of life, socio-economic histories, and cultural traditions.

This course provides an introduction to systems thinking and systems models in public health. Problems in public health and health policy tend to be complex with many actors, institutions and risk factors involved. If an outcome depends on many interacting and adaptive parts and actors the outcome cannot be analyzed or predicted with traditional statistical methods. Systems thinking is a core skill in public health and helps health policymakers build programs and policies that are aware of and prepared for unintended consequences. An important part of systems thinking is the practice to integrate multiple perspectives and synthesize them into a framework or model that can describe and predict the various ways in which a system might react to policy change. Systems thinking and systems models devise strategies to account for real world complexities. This work was coordinated by the Alliance for Health Policy and Systems Research, the World Health Organization, with the aid of a grant from the International Development Research Centre, Ottawa, Canada. Additional support was provided by the Department for International Development (DFID) through a grant (PO5467) to Future Health Systems research consortium. © World Health Organization 2014 All rights reserved. The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of the World Health Organization concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted and dashed lines on maps represent approximate border lines for which there may not yet be full agreement. The mention of specific companies or of certain manufacturers’ products does not imply that they are endorsed or recommended by the World Health Organization in preference to others of a similar nature that are not mentioned. Errors and omissions excepted, the names of proprietary products are distinguished by initial capital letters. All reasonable precautions have been taken by the World Health Organization to verify the information contained in this publication. However, the published material is being distributed without warranty of any kind, either expressed or implied. The responsibility for the interpretation and use of the material lies with the reader. In no event shall the World Health Organization be liable for damages arising from its use. Johns Hopkins University Bloomberg School of Public Health has a non-exclusive license to use and reproduce the material.

By the end of this project, you will be able to load textual data into R and turn it into a corpus object. You will also understand the concept of measures of readability in textual analysis. You will know how to estimate the level of readability of a text document or corpus of documents using a number of different readability metrics and how to plot the variation in readability levels in a text document corpus over time at the document and paragraph level. This project is aimed at beginners who have a basic familiarity with the statistical programming language R and the RStudio environment, or people with a small amount of experience who would like to learn how to measure the readability of textual data.