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DRPS : Course Catalogue : School of Social and Political Science : Postgrad (School of Social and Political Studies)

Postgraduate Course: Computational Sociology (PGSP11583)

Course Outline
SchoolSchool of Social and Political Science CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryThis course provides a broad introduction to computational techniques and their application in the empirical social sciences. It provides students with a sweeping introduction to the technical and/or statistical dimensions of different computational methods, hands-on examples of their use, and a venue for discussion of applied examples. The course will provide a venue for seminar discussion of examples using these methods in the empirical social sciences as well as lectures on the technical and/or statistical dimensions of their application.
Course description The course has two principal learning aims: 1) an in-depth introduction to both the background of computational techniques and more recent examples; 2) basic training in the application of computational techniques with the use of R or Python programming languages.

Computational social science has taken off as a field in recent years, as attested by recent review pieces in both sociology and general scientific journals (see and The course will give students the ability to apply basic examples of these techniques as well as the requisite background in the history of computational methodology. Lectures will be organized by thematic topic and will focus on technical and/or statistical dimensions of the technique in question. The topics covered may include, but are not limited to: 1) web-scraping and APIs; 2) social network analysis; 3) computational text analysis; 4) online surveys and experiments; 5) machine learning; 6) geocomputation. The first lecture will be dedicated to a broader introduction to the strengths and weaknesses of 'digital trace' data as well as a primer on ethical considerations of their use.

Seminars will be devoted to the discussion of applied examples. The seminars will be organized by thematic topic and readings will be selected that speak to the technique covered in the most recent lecture.

Each week students will be expected to attend the lecture or seminar, depending on the week as these will alternate. In seminar weeks, students will be expected to read all of 2-3 essential readings, which will form the basis for a class discussion.

Assessment will be in the form of a 4000 word summative response piece. Students will be given a recent piece of published research that employs one of the computational techniques discussed during the ten weeks of the course. They will be asked to act as 'reviewer' to the article, laying out in the style of an academic peer review the contents, methods, and claims of the article as well as their criticisms and recommendations for further analyses. In a second part of the assessment they will also be asked to reproduce some of the analyses contained in the article using either R or Python. They will also be asked to submit their code as part of the assessment.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Academic year 2022/23, Available to all students (SV1) Quota:  30
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Seminar/Tutorial Hours 20, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 176 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Summative response piece + code for analysis used (100%)
Feedback Coding choices can be discussed in tutorial settings with the TA.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Be competent in R or Python for implementing computational techniques
  2. Have knowledge of the recent applied literature in computational sociology
  3. Provide reproducible workflows for computational analyses with R Markdown or Jupyter notebooks
  4. Appraise the relationship between theory, measurement, and method in applied examples of computational techniques
  5. Appraise different data sources and assess the suitability of computational techniques for their analysis
Reading List
Salganik, Matthew. 2018. Bit by Bit: Social Research in the Digital Age. Princeton: Princeton University Press.
Lazer et al. 2020. 'Computational social science: Obstacles and opportunities.' Science.
Nelson, LK, 2020. "Computational Grounded Theory: A Methodological Framework." Sociological Methods & Research. 49(1):3-42.
Siegel, A. and V. Badaan. 2020. "#No2Sectarianism: Experimental Approaches to Reducing Sectarian Hate Speech Online." American Political Science Review 114(3): 837:855.
Garg et al. 2018. 'Word embeddings quantify 100 years of gender and ethnic stereotypes.'PNAS 115(16): E3635-E3644.
Additional Information
Graduate Attributes and Skills o Communication and IT skills
o Autonomy and self-directed research
o Reflection and critical faculties
KeywordsNot entered
Course organiserDr Chris Barrie
Course secretaryMrs Gillian MacDonald
Tel: (0131 6)51 3244
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