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Programming for the Social Sciences

Module description

Advancements in technology have not only led to our society becoming increasingly digital but has also resulted in the emergence of new research methodologies and sources of data. This practical-based module will provide you with both the programming skills and understanding of new research methodologies that is necessary in order conduct cutting-edge social science research using computational methods. Specifically, it will provide you with an introduction to coding in the Python programming language before proceeding to provide you with a working knowledge and understanding of a number of contemporary research methods such as natural language processing, computer simulation, social 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 introduce students to a number of computational research methods used in the social sciences that require coding abilities.

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 programming in Python.
• 2. Develop an understanding of the broader fundamental concepts underlying programming.
• 3. 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...

• 4. Demonstrate the ability to critically evaluate the relationships between data, research questions, and the subject of study.
• 5. Develop an understanding of wider concepts and factors in computational social science research; i.e. developing algorithms and data validity.
• 6. Critically reflect on the development of data sources and associated research methods in an increasingly digital society.

ILO: Personal and key skills

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

• 7. Demonstrate ability to work as part of a group on a joint project
• 8. Demonstrate written analytical skills by producing technical reports that detail the data and methods used in a piece of research.

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 Python
• Setting up a scientific computing environment
• Foundational programming concepts
• Sources of social data in the modern world
• Data structures
• Data processing
• Data exploration and visualisation
• Mathematical and statistical methods
• Algorithmic thinking
• Research design for computational research methods
• Application programming Interfaces (APIs)
• Web scraping
• Social networks and relational thinking
• Computer simulation
• Research ethics
• Probability
• Introduction to machine learning
• Generative models

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)

Indicative learning resources - Basic reading

Basic reading:

• McLevey, J. (2021) Doing Computational Social Science: A Practical Introduction Sage Publications Ltd: London
• 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
• Mesquita, E. & Fowler, A. (2021) Thinking Clearly with Data: A Guide to Quantitative Reasoning and Analysis Princeton University Press Woodstock.
• 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
• 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

Key words search

Python, data science, programming