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Computational Social Science 1

Module description

Technological advancements have not only driven the digitisation of society and the emergence of novel socio-political issues, but have also resulted in significant developments in algorithms, computational power, and increasingly large datasets. This practical-based module will provide you with both the technical programming skills and understanding of data science techniques that you will need to research pre-existing and novel social-political and economic issues. Specifically, it will introduce you to the Python programming language, assuming zero prior-experience, and give you the skills necessary to use it for data analysis.

Module aims - intentions of the module

This module has two main aims. The first is to introduce you to the Python programming language and to the fundamental concepts underlying programming in general. This includes, but is not limited to, variables, coding architecture, iteration operations, file input/output, data structures, plotting data, numerical and statistical techniques, and importable packages. The second aim of the module is to build upon the first and train you in how to use Python for data analysis.

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

• 1. Demonstrate a good understanding and practical knowledge of the Python programming language.
• 2. Demonstrate the ability to utilise certain computational methods to conduct a piece of research.

ILO: Discipline-specific skills

On successfully completing the module you will be able to...

• 3. Demonstrate the ability to critically evaluate the relationships between data, research questions, and the subject of study.
• 4. Develop an understanding of wider concepts and factors in computational social science research; i.e. developing algorithms and data validity.

ILO: Personal and key skills

On successfully completing the module you will be able to...

• 5. Demonstrate written analytical skills and data analysis skills using Python.
• 6. Demonstrate the ability to link a piece of data analysis back to a substantive research question in a meaningful manner.

Syllabus plan

Whilst the module’s precise content may vary from year to year, it is envisaged that the syllabus will cover some or all of the following themes:

• Introduction to programming languages
• Setting up a scientific computing environment
• Foundational programming concepts
• Introduction to Python
• Python syntax
• Coding architecture
• Data structures
• Data processing
• Data exploration and visualisation
• Mathematical and statistical methods

Learning activities and teaching methods (given in hours of study time)

Details of learning activities and teaching methods

Formative assessment

Summative assessment (% of credit)

Details of summative assessment

Details of re-assessment (where required by referral or deferral)

Re-assessment notes

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 40%) you will be required to redo the assessment(s) as defined above. If you are successful on referral, your overall module mark will be capped at 40%.

Indicative learning resources - Basic reading

• Edelmann, A., Wolff, T., Montagne, D. & Bail, C. (2020) ‘Computational Social Science and Sociology’ Annual Review of Sociology 46(1): 61-81 https://doi.org/10.1146/annurev-soc-121919-054621
• McLevey, J. (2021) Doing Computational Social Science: A Practical Introduction Sage Publications Ltd: London
• Mesquita, E. & Fowler, A. (2021) Thinking Clearly with Data: A Guide to Quantitative Reasoning and Analysis Princeton University Press Woodstock.
• Van Atteveldt, W., Trilling, D. & Calderon, C. (2022) Computational Analysis of Communications: A Practical introduction to the Analysis of Texts, Networks, and Images with Code Examples in Python and R Wiley Blackwell: Chichester
• Zhang, J., Wang, W., Lin, Yu-Ru, & Tong, H. (2020) ‘Data-Driven Computational Social Science: A Survey’ Big Data Research https://doi.org/10.1016/j.bdr.2020.100145

Key words search

Computational social science; Social data science; Data science; Research methods; Python; Data