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Member rate £492.50
Non-Member rate £985.00
£70 off Early Bird for Members until 31/10/2024
Save £45 Loyalty discount applied automatically*
Save 5% on each additional course booked
*If you attended our Methods School in the last calendar year, you qualify for £45 off your course fee.
Date: Monday 24 – Friday 28 March 2025
Time: 10:00 – 13:00 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 masters level, PhD students, and postdoctoral researchers. The course 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. The instructor will also conduct live Q&A sessions and offer designated office hours for one-to-one consultations.
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.
As a participant in this course, you will engage in a variety of learning activities designed to deepen your understanding and mastery of the subject matter. While the cornerstone of your learning experience will be the daily live teaching sessions, which total three hours each day across the five days of the course, your learning commitment extends beyond these sessions.
Upon payment and registration for the course, you will gain access to our Learning Management System (LMS) approximately two weeks before the course start date. Here, you will have access to course materials such as pre-course readings. The time commitment required to familiarise yourself with the content and complete any pre-course tasks is estimated to be approximately 20 hours per week leading up to the start date.
During the course week, you are expected to dedicate approximately two-three hours per day to prepare and work on assignments.
Each course offers the opportunity to be awarded three ECTS credits. Should you wish to earn a 4th credit, you will need to complete a post-course assignment, which will involve approximately 25 hours of work.
This comprehensive approach ensures that you not only attend the live sessions but also engage deeply with the course material, participate actively, and complete assessments to solidify your learning.
This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.
By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.