Collecting and Analysing Big Data for Social Sciences: an Introduction

About

Prerequisite Knowledge
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.

Learning commitment
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.

The students are expected to prepare the readings of the lectures and to study the course material and exercises after the collective classroom meetings.

In depth
This course is an introduction to collecting and analysing big data, specifically addressed to social scientists.

Day 1
• 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;

Day 2
Introduction to supervised and unsupervised learning covering different algorithms;

Day 3
Introduction to text as data;

Day 4
Introduction to automatic text analysis, including dictionary approaches and topic models;

Day 5
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. The instructor will also conduct live Q&A sessions and offer designated office hours for one-to-one consultations.

Course leader

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.

Target group

The course is designed for masters level, PhD students, and postdoctoral researchers

Course aim

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.

If you're interested in this course, registration opens on 01/10/24 and you can register here: https://ecpr.eu/Events/Event/PanelDetails/15638

Fee info