Undergraduate Course: Understanding Society with Big Data: Computational Social Science (INFR08034)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 8 (Year 1 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course introduces the fundamentals of computational social science, focusing on how to study social phenomena and human behaviour through the analysis of big social data, such as social media datasets.
The course does not involve any coding (programming). It is highly practical, providing students with hands-on experience in social data analysis, including textual, network, and statistical analysis, using user-friendly tools provided during the course. It is designed for students from diverse disciplines, including but not limited to Informatics, Social and Political Science, Business, and Philosophy, Psychology and Language Sciences. Students will apply their learning by completing a final project in small, cross-disciplinary groups, integrating different perspectives to address real-world social science questions. |
| Course description |
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 Social Science
Quantitative and Qualitative Concepts
Data Types and Collection Methods
Data Cleaning and Preprocessing
Ethics and Fairness in Computational Social Science
Data Visualization
Network Analysis
Text Analysis for Social ScienceMachine Learning Tools for Social 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.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | This class takes an inclusive approach in which knowledge of computer programming will not be a requirement or a learning objective. The class is also relatively low on maths requirements, but some fundamental numeracy skills are necessary. Brushing up on fractions, variables, and graphing x-y plots is recommended if you might struggle with these. |
Information for Visiting Students
| Pre-requisites | This class takes an inclusive approach in which knowledge of computer programming will not be a requirement or a learning objective. The class is also relatively low on maths requirements, but some fundamental numeracy skills are necessary. Brushing up on fractions, variables, and graphing x-y plots is recommended if you might struggle with these. |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2025/26, Available to all students (SV1)
|
Quota: 50 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 20,
Seminar/Tutorial Hours 5,
Supervised Practical/Workshop/Studio Hours 15,
Feedback/Feedforward Hours 5,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
149 )
|
| Assessment (Further Info) |
Written Exam
50 %,
Coursework
50 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Written Exam __50__%
Coursework __50__%
Coursework will involve self-directed work in an interdisciplinary group on topic and datasets in computational social science. This will measure the practical application of the learning objectives. No programming (coding) is required. |
| Feedback |
There will be a lab every two weeks and a tutorial every two weeks with questions and model solutions so you can see how well you are doing. Tutors and demonstrators will be present to help students who get stuck and explain solutions.
We will have an online course forum where you can ask questions and engage in discussions with your peers and the instructors. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Formulate research questions about social phenomena and human behaviour
- Identify the appropriate big data and computational methods to answer these questions, taking into account ethical challenges and potential biases.
- Apply basic data analysis and descriptive statistics on big (e.g. textual, network) data
- Explain findings and their implications and limitations to a wide audience.
- Collaborate responsibly and effectively in an interdisciplinary setting
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Reading List
Salganik, Matthew J. Bit by Bit: Social Research in the Digital Age. Princeton: Princeton University Press, 2018. Print.
https://www.bitbybitbook.com/
Foster, Ian, Ghani, Rayid, Jarmin, Ron S., Kreuter, Frauke, Lane, Julia. Routledge, 2021. Big Data and Social Science: Data Science Methods and Tools for Research and Practice.
https://textbook.coleridgeinitiative.org/
(others to be discussed) |
Additional Information
| Graduate Attributes and Skills |
Research and enquiry: Problem solving, create, identify and evaluate options in order to solve complex problems. Analytical thinking, analyse, synthesise, critically and methodically appraise thoughts to break down complex problems into manageable components.
Personal and intellectual autonomy: Decision making, being able to make, implement and review decisions based on appropriate techniques. Ethics and social responsibility, develop reflective awareness of ethical dimensions, and responsibilities to others, in work and everyday life.
Personal effectiveness: Team working, effectively perform within team environments including the ability to recognise and capitalise on individuals' different thinking, experience and skills. Assertiveness and Confidence, acquire skills for working in teams and groups, and leading where appropriate.
Communication: Written communication, articulating and effectively explaining information. |
| Keywords | CSS,Computational Social Science,Social Data Science |
Contacts
| Course organiser | Dr Tj Elmas
Tel:
Email: TJ.Elmas@ed.ac.uk |
Course secretary | Ms Kendal Reid
Tel: (0131 6)50 5194
Email: kr@inf.ed.ac.uk |
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