Summary of content

This module offers an introduction at postgraduate level, to data analytics. Core topics focus on data acquisition, analysis and interpretation. The module includes introductory learning on how to deal with data types, theories of big data acquisition and formatting—how do you deal with data in large amounts data mining, predictive analysis, supervised and unsupervised learning. Students will then be introduced to descriptive strategies for data using software including excel and SPSS and Bloomberg as sources of big data. Interpretative strategies for big data will include understanding graphic visualisation, interpretation of results, causality and its relationship(s) to data and summarization, decision trees and report writing strategies.

This module embeds equality, diversity and inclusion perspectives throughout the curriculum. Students critically assess issues of algorithmic bias, problematic data representations, privacy concerns, and the potential for marginalization or harm. Student develop data skills that allow a critical understanding of the ethics of quantitative data analytics.

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Assessment methods