Course Catalogue

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The course introduces data science applications in the fields of education and social science.  

Lecture - 1 hour/week, Labs - 1 hour/week.

Mandatory Entry Requirements

Admission to the joint degree in Research and Data Analysis or to the "with Quantitative Methods" degree in any subject.

Recommended Entry Requirements

Grade point average of 9 (D3) over QM1 and QM2, or Stats 1Y and 1Z.

The summative assessment will consist of:

•A research report based on an analysis of a provided quantitative or qualitative datasets. You will have been working towards this report in your computer labs. The report will be 3,000-3,500 words in length plus appropriate analysis output (tables and graphs). The research report counts for 75% of the final mark.

•Digital abstract based on the research report. You will be asked to produce a digital abstract outlining the main points of your research report. The abstract can be created using various digital tools, such as, for example, a video, a podcast, an infographic, a poster. The digital abstract counts for 25% of the final mark.

This course will provide an intersection between contemporary issues in educational research, focusing on education inequalities, and data science applications using R and RStudio. It will provide you an opportunity to further your research design and introduce analysis skills employing principles of open science, such as working with open access datasets and designing reproducible workflows. You will be introduced to tasks and techniques employed in data science, such as processing data, visualising and modelling data and communicating findings using examples from both quantitative and qualitative datasets. Specific topics include theoretical foundations and empirical evidence on social inequalities in education, what are data science applications in education, using aggregated data to illustrate education inequalities, text analysis of qualitative data.

By the end of this course students will be able to:

■ Demonstrate critical understanding and the ability to evaluate the theoretical foundations and relevance of empirical evidence in the socio-economic inequality in education

■ Critically review the appropriate and effective application of data science techniques and methods to address theoretical questions

■ Apply appropriate methods and use R and RStudio to process and analyse aggregate quantitative data and qualitative textual data

■ Interpret, discuss and communicate the results of analysis through the appropriate visualisations and narratives

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.