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CSOC10010

Academic Year 2025/2026

Introduction to Computational Social Science (CSOC10010)

Subject:
Computational Social Science
College:
Social Sciences & Law
School:
Sociology
Level:
1 (Introductory)
Credits:
5
Module Coordinator:
Dr Scott Renshaw
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module provides an introduction to key concepts in computational social science (CSOC). It describes how CSOC builds on traditional social science and how it transcends it. Students will learn about: (i) the distinct type of problems CSOC addresses, the data it employs, and the explanations it produces, and (ii) the role that CSOC has played in the development of new methods (for example, computer simulation) and alternative approaches to standard methods (for example, experiments) in the social sciences.

The first two weeks offer an overview of the theoretical-methodological framework of CSOC. The following weeks cover six popular methods (one each week) that deal in distinctive ways with qualitative, quantitative, and also synthetic data. This will be done through various contexts, e.g.: design of experiments, approaches to computer simulation, network analysis techniques, text mining, and spatial analysis. The ethical practice of CSOC is discussed last and, when appropriate, also within the previous weeks focused on different methods.

Topics will be covered combining lectures with a variety of practical class activities. Students are encouraged to prepare and actively participate in both.

About this Module

Learning Outcomes:

On completion of this module student will be able to:
· Identify the main features of CSOC.
· Explain what problems are better suited to be studied through computational methods.
· Characterise methodological tools available in CSOC.
· Critically analyse potential applications of CSOC.

Student Effort Hours:
Student Effort Type Hours
Autonomous Student Learning

92
Lectures

20
Total

112

Approaches to Teaching and Learning:
- Lectures
- Group work
- Active/task-based learning
- Critical writing
- Reflective learning;

Requirements, Exclusions and Recommendations
Learning Requirements:

No Prior Learning required


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade Component repeat (in-module) Offered
Assignment(Including Essay): PeerScholar Phases 1 (Create) & 2 (Assess):
1) Written assignment.
2) Peer evaluation of your classmates' submissions.
Week 6, Week 7 Standard conversion grade scale 40% No
15
 No
Quizzes/Short Exercises: Multiple-choice questionnaire on the methods covered in the module. Week 12 Standard conversion grade scale 40% No
25
 No
Reflective Assignment: PeerScholar phase 3 (Reflect):
Written assignment building on the first two phases.		 Week 12 Standard conversion grade scale 40% No
60
 No

As part of UCD's student support, under the Additional Consideration policy, extensions may be available for the following assessments in the module: Assignment (including essay/poster), Portfolio, Reflective Assignment, Report(s), and Individual Project.


Carry forward of passed components
No
 

Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Professor Taha Yasseri Lecturer / Co-Lecturer
Dr Arjumand Younus Lecturer / Co-Lecturer
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