Module Directory

With research methods rapidly changing in response to the large-scale generation of data within society, social science needs to ensure it is engaged with new digital methods to both benefit from them and to shape them. This module is designed for first-time programmers, from social science and humanities backgrounds, and focuses on teaching foundational skills in data collection, analysis, and visualisation using the Python programming language.

Students will learn key skills such as how to gather large-scale datasets from application programming interfaces (APIs), data cleaning and exploratory data analysis, and visualisation for finding patterns and themes, in varied datasets. Students will develop an understanding of how, through these techniques, new, creative non-traditional data sources can be utilised for social science research.

The aims of this module are:

• To provide students with a fundamental knowledge of the Python programming language


• To develop the ability of students to acquire data through application programming interfaces (APIs)


• To provide students with knowledge and understanding of cleaning, managing and reporting on large datasets


• To provide knowledge of how these techniques are utilised in research as well as in non-academic contexts

By the end of this module, students will be expected to be able to:

1. Have a fundamental proficiency in the Python programming language.


2. Generate new datasets using an API.


3. Store, query and clean large datasets of varied data.


4. Understand different approaches to data visualisation and how they may impact interpretation.


5. Clearly communicate method and findings both through visualisations and written reports.


6. Understand the ethical and legal dimensions of computational social science.


7. Situate computational techniques within broader principles of research design in the social sciences.

In this module, students will learn to combine their growing knowledge about society, social processes and research design, with powerful tools to both draw on and analyse the vast amounts and forms of new data in a way that is critical, ethical and valuable.

This module provides a practical introduction to a range of methods that utilise intensive computational processing. Students will be taught in Python, a general-purpose accessible programming language popular in data science and used across a vast range of sectors. Students are not expected to have any prior programming experience, making this a valuable opportunity to learn new research techniques, as well as a skill that is in great demand.

Indicative Topic list:

• Topic 1 - What is computational social science? Let’s get started.


• Topic 2 - Python Fundamentals 1: Loops, Lists and Strings, oh my! 


• Topic 3 - Python Fundamentals 2: Functions, Errors and how to fix them.


• Topic 4 - Pandas 1: Data Wrangling with Pandas.


• Topic 5 - Pandas 2: Cleaning, transforming and storing your data.


• Topic 6 - Data Visualisation 1: Plot fundamentals.


• Topic 7 - Data Visualisation 2: How to explore data through visualisation.


• Topic 8 - APIs 1: Pulling Data from the Guardian API.


• Topic 9 - APIs 2: Wrangling and visualising your own data.


• Topic 10 - Catch up and code surgery.

This module will be delivered via one x two-hour seminar per week which will be delivered face-to-face and attendance is expected.