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.

In addition to core computational concepts, the class exercises will focus on the following topics

1. Gathering data: Learning how to collect and scrape data from websites as well as working with APIs.

2. Data manipulation tools: Learning how to go from unstructured data to a dataset ready for analysis. This includes to import, preprocess, transform and merge data from various sources.

3. Visualization tools: Learning best practices for visualizing data in different steps of a data analysis. Participants will learn how to visualize raw data as well as effective tools for communicating results from statistical models for broader audiences.

4. Prediction tools: Covering key implementations of statistical learning algorithms and participants will learn how to apply and interpret these models in practice.

Target group

Bachelor/Master

Course aim

After the course the student should be able to:

Knowledge:
•Use computational methods and social data in the field of the state of the art social science literature.
•Use different kinds of data (survey, webbased, experimental, administrative, etc.) to answer various questions in the social sciences and have strong knowledge of advantages and challenges.
•Have an overview of key benefits and challenges of working with different kinds of social data.
•Know strengths and weaknesses of statistical prediction algorithms as well as the ability to estimate these models in practice

Competences:
•Present modern data science methods needed for working with computational social science and social data in practice.
•In practice to write and debug code, to clean, transform, scrape, merge, visualize and analyze social data.

Skills:
•Generating new data by collecting and scraping web pages (import and export data from numerous sources).
•Work with APIs and have basic knowledge of functional programming.
•Have strong practical data science skills and effective data visualization skills.
•Discuss ethical challenges related to the use of different types of data.
•Discuss how prediction tools relate to existing empirical tools within economics such as causal inference and regression.

Fee info