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In an increasingly data-driven world, programming skills are much sought after by employers seeking graduates to work in their analytical teams. This module will offer a grounding in the Python Programming Language, which has become one of the most widely used for social data analysis. While R programming is arguably more suitable for applied (social science) statistics, Python is more readily deployed for machine learning and the possibilities of algorithmic modelling. Machine learning has gained momentum within the social sciences, this can be seen in both the number of publications using machine learning techniques and the increased popularity of machine learning as a teaching subject. Thus, it is important for social scientists to explore, and consequently take advantage of, what machine learning offers.Ìý

Due to the wide range of applications for machine learning within the social sciences, the module is suitable for students from all disciplinary backgrounds, but students enrolling on the module will require knowledge of fundamental statistical concepts such as linear regression as it is a foundation for gaining an understanding of the more advanced machine learning techniques. Previous experience of using a programming language (such as R) is desirable but not essential.

Broadly, the module comprises three parts: data preparation and wrangling for machine learning (including fundamental machine learning concepts); unsupervised machine learning techniques (e.g., K-means and hierarchical clustering, principle component analysis) and supervised machine learning techniques (e.g., linear and logistic regressions, tree-based models, support vector machines).

It will be problem-focused and mimic the kinds of analysis students are likely to undertake both in their independent research (such as dissertations) but also in the workplace once they’ve graduated. Students will leave the module empowered to apply, interpret and critically analyse machine learning techniques using Python and they will also be able to critically engage with the results from such analysis and recognise their added value.Ìý

Pre-requisites: Please note that students should have completed one of the following: POLS0008, POLS0083, ECON0004, ECON0005, ECON0019, ECON0020, GEOG0016, GEOG0030, GEOG0027 before taking this module.