9 February 2024
Collecting and Analysing Big Data for Social Sciences: an Introductiononline course
"Learn how to collect and analyse big data, apply current methods for analysing textual data, link machine learning methods to relevant social science questions and program in R.
Need to know
Students should have a basic knowledge of R for data management and data-analysis. For students with no prior experience with R, a self-study package will be made available upon request (please contact the instructor). Students should have basic knowledge of exploratory univariate and bivariate statistics and be acquainted with standard regression techniques.
The students are expected to prepare the readings of the lectures and to study the course material and exercises after the collective classroom meetings.
This course is an introduction to collecting and analysing big data, specifically addressed to social scientists.
• Definition of big data and types of big data (for social sciences);
• Opportunities of working with big data for social sciences;
• Limitations of big data for social sciences;
Introduction to supervised and unsupervised learning covering different algorithms;
Introduction to text as data;
Introduction to automatic text analysis, including dictionary approaches and topic models;
Introduction to webscraping and use of API for automatic data collection.
Hands-on introduction and intermediate programming in R.
The material will be illustrated with examples from social science research.
How the course will work online
During the lectures, the material will be introduced in a comprehensible and non-technical way, followed by a demonstration on how to conduct the methods in R.
You will be expected to go over the R demonstrations and accompanying exercises after each class independently. Afterwards a Q&A will be organised during the lab sessions."
Cecil Meeusen is an assistant professor in Social Data Science at the Center for Sociological Research (KU Leuven) where she teaches courses on data-analysis and big data with a specific focus on applications in the field of social science.
The course is designed for a demanding audience (researchers, professional analysts, advanced students).
"This course will provide you with an introduction to collecting and analysing big data for social scientists. The course offers you an introduction to statistical techniques and programming skills for the collection, analysis and presentation of big data. As such, this is not a course on very advanced machine learning techniques, data mining or SQL. The course applies the big data techniques specifically to cases in the social sciences (political science, sociology, communication science, social policy).
By the end of the course, it is expected that you will be able to
-Collect data from the internet using basics of web scraping and APIs;
-Read and analyse digital text files;
-Analyse data using supervised learning techniques;
-Analyse data using unsupervised learning techniques;
-Understand and apply current methods for analysing textual data;
-Link machine learning methods to relevant social science questions;
-Critically assess the use of big data for social sciences;
-Program in R."
"You can earn up to four credits for attending this course.
3 ECTS credits – Attend 100% of live sessions and engage fully with class activities.
4 ECTS credits – Attend 100% of live sessions, engage fully with class activities and complete a post-class assignment."
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