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CSOC20010

Academic Year 2025/2026

Applied Computational Social Science (CSOC20010)

Subject:
Computational Social Science
College:
Social Sciences & Law
School:
Sociology
Level:
2 (Intermediate)
Credits:
5
Module Coordinator:
Dr David Anzola
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 encourages students to critically reflect on what it means to be a computational social scientist and what the professional practice of computational social science entails. We will engage in hands-on practical learning with two computational methods: agent-based social simulation and co-word analysis, to support individual and collective exploration of: (i) the disciplinary and professional differences between computational social science and mainstream social and computer science, (ii) the current and future labour market for computational social scientists, (iii) the knowledge and skills necessary for professional practice in the field, and (iv) the professional impacts of the progressive digitalisation of society.

About this Module

Learning Outcomes:

On completion of this module, students will be able to:
• Identify core methodological features of agent-based social simulation and co-word analysis.
• Manipulate code and parameters in computational models.
• Examine factors influencing accuracy and performance in computational research.
• Differentiate professional competence pathways in computational social science.

Student Effort Hours:
Student Effort Type Hours
Lectures

24
Autonomous Student Learning

104
Total

128

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

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Pre-requisite:
CSOC10010 - Intro Comp Soc Sci


 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Participation in Learning Activities: Continuous assessment comprising in-class activities centred on the design and manipulation of computational models. Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10, Week 11, Week 12 Standard conversion grade scale 40% No
20
 No
Assignment(Including Essay): Written assignment reflecting on the representational capacity of digital technologies and the (digital) data challenges in contemporary society. Week 8 Standard conversion grade scale 40% No
40
 No
Student Negotiated or Choice of Assessment: Video (individual) reporting the findings of a co-word analysis
OR
Podcast (two people) discussing the professional -computational- skills set demanded by the contemporary workplace.		 Week 12 Standard conversion grade scale 40% No
40
 No

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.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Lecture Offering 1 Week(s) - 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12 Mon 12:00 - 13:50
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