apply descriptive, predictive, and prescriptive analytics (supported by visualisation techniques) in order to explore data, as well as develop models,
The general aim of this coursework is for students to apply descriptive, predictive, and prescriptive analytics (supported by visualisation techniques) in order to explore data, as well as develop models, which ultimately contribute to data-driven decision- making for two different problem domains and data sets (LO1, LO2, LO4). Students are expected to document and present their findings in a 4,000 word report that is worth 100% of their grade.
All data, as well as the saved R scripts that highlight data manipulation and management must best ored and submitted along with the documentation (LO3). In more detail, the documentation should be split into three sections:
- Section one requires the use of inferential statistics and dimension reduction techniques in order to extract components from a survey and use the component scores in several analyses (LO1). Students are expected to critically analyse their results, manage and manipulate the data (LO3), as well as illustrate their findings using visualisation techniques (LO2).
- Section two requires students to train at least two types of machine learning algorithms (or regression models) in order to support data-driven decision making (LO4).
- Section three requires students to report on the results of sections one and two in a document that presents the findings for a layman audience, with explicit recommendations based on the analyses (LO4). This section needs to be particularly rich in visualisation (LO2)
