Data Analytics for a Datafied Society

Learn data analytics in the social sciences and humanities to strengthen your scientific skills, enhancing your professional and academic career prospects.

This minor is designed for students interested in learning data analytics and digital research methods from a social science and humanities perspective. It combines theoretical insights and practical skills, focusing on how to collect, analyze, and interpret digital data using programming languages (in particular the programming language R). As computational research skills are becoming increasingly important on the labor market, the minor helps students to strengthen both their professional and academic job prospects.

Through courses in Computational Text Analysis and Network Analysis, students will gain practical experience in applying data science techniques to real-world research projects in the social sciences and humanities.

The minor also provides a strong theoretical foundation, exploring the historical and contemporary role of digital networks, social media, and the internet within our increasingly datafied society. By the end of the program, students will have developed the critical and technical skills needed to carry out computational research in today’s data-driven world.
The minor (15 ECTS) consists of the following three courses:

• Histories of the Networked World
• Computational Text Analysis
• Applied Network Analysis

Upon completion of the minor, students achieve the following key learning objectives:

Module 1: Histories of the Networked World
At the end of this module, students will be able to:

• Trace key milestones in the development of the networked world from the 1960s to the present.
• Analyze global disparities in access to and usage of digital infrastructures over time.
• Critically assess their own use of digital networks within a global context.

Module 2: Computational Text Analysis
At the end of this module, students will be able to:

• Write basic code in the R programming language.
• Apply and critically assess text analysis techniques such as frequency analysis, topic modeling, and sentiment analysis.
• Collect and scrape textual data from online platforms and websites.
• Analyze, visualize, and interpret textual data using R.

Module 3: Applied Network Analysis
At the end of this module, students will be able to:

• Explain key concepts and theories in social network analysis.
• Extract network data from online platforms and websites.
• Visualize network data using appropriate metrics.
• Effectively communicate and interpret findings from network analysis.

Prior knowledge :
Students should have a basic proficiency in English. No prior experience with Excel or programming is required for this minor. Students who are interested in learning programming fundamentals are encouraged to enroll.

Attendance:
This minor follows the attendance policy set by the Erasmus School of History, Culture, and Communication, as outlined in the Teaching and Examination Regulations (TER) (see Article 3.5 – Attendance and Participation Requirements). Attendance and active participation in seminars are mandatory. Students can only complete the course if they meet the attendance and participation requirements.

Teaching Method
The minor consists of three modules. For each module, students are required to attend weekly seminar sessions.

Teaching Material
Throughout the minor, the following teaching materials will be used:

• Literature : Handbook and shared readings, provided free of charge.
• Software : R, RStudio, Gephi (all open-source tools).

Method of examination
Module 1: Histories of the Networked World

• Research paper
• Written exam

Module 2: Computational Text Analysis

• Research paper
• Group project

Module 3: Applied Network Analysis

• Presentation

• Report

Composition of final grade
Module 1: Histories of the Networked World

• Research paper (50%) (I)
• Written exam (50%) (I)

Module 2: Computational Text Analysis

• Research paper (70%) (I)
• Group project (30%) (G)

Module 3: Applied Network Analysis

• Presentation (30%) (G)
• Report (70%) (I)