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Member rate £492.50
Non-Member rate £985.00
Save £45 Loyalty discount applied automatically*
Save 5% on each additional course booked
*If you attended our Methods School in the July/August 2023 or February 2024.
Date: Monday 5 – Friday 9 February 2024
Duration: 3 hours of live teaching per day
Time: 09:30 – 12:30 CET
This course will provide you with a highly interactive online teaching and learning environment, using state of the art online pedagogical tools. It is designed for a demanding audience (researchers, professional analysts, advanced students) and is capped at a maximum of 16 participants so that the teaching team can cater to the specific needs of everyone.
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
4 credits - Engage fully in class activities and complete a post-class assignment
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. She conducts research in the field of political sociology and intergroup relations.
This course is an introduction to collecting and analysing big data, specifically addressed to social scientists.
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.
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.
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.