CSS: Understanding Society with Big Data: Computational Social Science

Welcome to Understanding Society with Big Data: Computational Social Science

Learning Outcomes

On completion of this course, you will be able to:

1. formulate research questions about social phenomena and human behaviour
2. identify the appropriate big data and computational methods to answer these questions, taking into account ethical challenges and potential biases.
3. apply basic data analysis and descriptive statistics on big (e.g. textual, network) data
4. explain findings and their implications and limitations to a wide audience.
5. collaborate responsibly and effectively in an interdisciplinary setting

Course Outline

The principal aim of the course is to teach how to conduct a computational social science project by introducing all the necessary components: formulating questions, collecting data, designing experiments, employing methods of artificial intelligence and text analysis and considerations of bias, fairness and ethical implications. 

Indicative list of topics: 

• Fundamentals of Computational Social Science
• Quantitative & Qualitative Concepts
• Data & Methods & Exploration
• Machine Learning
• Ethics & Fairness
• Network Analysis
• Text Analysis
• Large Language Models (LLMs)
• Data Sources & Collection
• Story-building for Data Science

The course will be taught through a mix of lectures, where new content will be introduced, and labs and tutorials where you will get hands-on experience putting these concepts into practice.

Lecturers

School of Informatics

• Tuğrulcan Elmas
• Björn Ross
• Walid Magdy

School of Political & Social Science

• Clare Llewellyn
• Tod Van Gunten
• Peaks Krafft