Introduction to social data science

Description

The objective of this course is to learn how to analyze, gather and work with 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 tools and methods for the following topics:

1. Gathering data: Learning how to scrape data directly through content in web pages on the internet as well as interacting with application programming interfaces (API).

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, including text data.

3. Data analysis: Learning best practice when visualizing and describing data in different steps of a data analysis. Participants will learn how to implement statistical learning algorithms and how to apply these for prediction and interpret these models in practice.

Learning Outcome
After completing the course the student is expected to be able to:

Knowledge:

Understand how and what data that can be used to answer typical 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.
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 statistical inference.

Skills:

Use data manipulation and data visualization to clean, transform, scrape, merge, visualize and analyze social data.
Parse and structure text data and conduct basic analysis.
Construct new datasets by scraping web pages and work with data APIs.
Estimate, apply and interpret machine learning algorithms and models in practice.
Conceptualize and execute projects in social data science.

Competences:

Independently master and implement computational methods and methods for working with social and behavioral data in the social science literature.
Present modern data science methods needed for working with computational social science and social data in practice.
Ensure legal and ethical procedures for data collection and management are satisfied.

Københavns Universitet