Rapid increases in computational power and the explosion of Internet and mobile phone use have radically transformed our lives and the way we communicate. This technological revolution has also fundamentally changed social sciences. "Big Data", simulation models and Web experiments offer social scientists the opportunity to address core social questions in new ways that were unimaginable just a decade ago. For example, economists now study marriage markets by extracting information from online dating websites, demographers track migrations using geo-located data from mobile phone or e-mail providers, sociologists explore social behavior and attitudes that emerge from blog posts and Twitter tweets, epidemiologists predict the spread of infectious diseases from Web searches, the United Nations use digital records to monitor socio-economic crises, etc. Increased computational power also allows social scientists drawn from all disciplines to develop large-scale agent-based and microsimulation models in order to gain insights into complex phenomena like segregation, kinship structure and the marriage market.
In this course, we will study how traditional methods used in social sciences can help us make sense of new data sources, and how these new data sources may require new approaches and research design. There will be a mix of lectures, student-led discussions, and hands-on computational activities (e.g., how to access and analyze data from social media platforms like Twitter and Facebook, how to approach large data sets, etc.).
We will discuss a number of substantive topics related to the emergence of (big) data-driven discovery in social sciences, with emphasis on population processes. By the end of the course, students will be familiar with relevant literature at the intersection of demographic research and computational social science. The main goals of the course are i) to develop critical thinking about the emergent field of big data analysis ii) to learn some of the methods, approaches and tools of big data analysis iii) to identify research questions in your own area of interest that could be addressed with innovative data sources and to devise an appropriate research plan.
Students in this class have different backgrounds. Some students are pursuing a PhD, some others are enrolled in an MA program. Some students may have strong computational and statistical skills, some others may not. Some students may be familiar with population studies, some others not. To accommodate the range of backgrounds, I emphasize substance, and key statistical and computational concepts. There will also be different types of homework assignments. Some of them will involve computing and coding. Some others will be critical reflections about the readings. In short, I facilitate and encourage the participation of students who do not have extensive background in statistics, or computational methods, but are eager to learn.