Main lecturers: Andreas Bjerre-Nielsen and Snorre Ralund.
Co-lecturer: David Dreyer Lassen.
The objective of this course is to learn how to analyze, gather and work with modern quantitative social science data. Increasingly, social data - data that capture how people behave and interact with each other - is available online in new, challenging forms and formats. This opens up the possibility of gathering large amounts of interesting data, to investigate existing theories and new phenomena, provided that the analyst has sufficient computer literacy, while at the same time being aware of the promises and pitfalls of working with various types of data.
This aim of this course is fivefold:
We will introduce students to the state of the art social science literature using computational methods and social data.
We will present students with an overview of key benefits and challenges of working with different kinds of social data.
We will show how various kinds of data (survey, web-based, experimental, administrative, etc.) can be used to answer different questions within the social sciences. Furthermore, we will discuss ethical challenges related to the use of different types of data.
We will introduce students to statistical techniques for predicting and classification, known as machine learning, and we will discuss how these methods relate to existing empirical tools within economics such as causal inference and regression.
We will present modern data science methods needed for working with computational social science and social data in practice. Being an effective economist and data scientist means spending large fractions of our time writing and debugging code. In this section you will learn how to write code that will clean, transform, scrape, merge, visualize and analyze social data.