Introduction to Social Data Science

About

The objective of this course is to learn how to analyze, gather and work with modern quantitative social science data. Increasingly, social 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.

Target group

Bachelot/Master

This course is available to students and pracitioners who are interested in social data science.

Because the course builds on a wide range of techniques, we do not have any hard requirements,but students are expected to have an interest in at least one of the following: Statistics, econometrics, linear algebra and a scripting language (we will focus on Python in this course).

Course aim

Knowledge:

- Understand use cases for different kinds of data (survey, webbased, experimental, administrative, etc.) to answer various
questions in the social sciences.

- Account for benefits and challenges of working with different kinds of social data.

- Identify and account for strengths and weaknesses of linear statistical prediction algorithms and estimate these models in
practice.

- Discuss ethical challenges related to the use of different types of data.

- Discuss how prediction tools relate to existing empirical tools within social sciences such as linear regression for inference.

Skills:

- Program in basic Pythion, write and debug code.

- Use data manipulation and data visualization to clean, transform, scrape, merge, visualize and analyze social data.

- Generate new data by collecting and scraping web pages (import and export data from numerous sources) and work with data
APIs.

- Apply and interpret machine learning algorithms and models in practice.

Competences:

- Independently master and implement computational methods and social data in the field of the state of the art social science
literature.

- Present modern data science methods needed for working with computational social science and social data in practice.

Fee info