This module is 100% assessed with coursework that must be submitted before 12:00 on Friday, 13 January 2023.
You are advised to refer to the assessment structure which also includes a template RStudio project for formatting your submission.
The table below will help you match up the colour coded course timetable with the different parts of the assessment criteria.
Part A) Open health data and anonymisation
The written sections of the coursework will require you to discuss the importance of open health datasets in healthcare applications, and the importance of data anonymisation.
We will cover several case studies where data was successfully deanonymised. You must explain some of these case studies in the assessment.
The code portion of the coursework will require you to work with a survey dataset. You will need to clean, wrangle and visualise the survey data.
Part B) Algorithms and health data
The written sections of the coursework will require you to discuss the roles of fairness, accountability, and transparency in algorithm development and application.
We will cover several case studies about algorithm "fairness" and applications. You must explain some of these case studies in the assessment.
We will also discuss several statistical/data science topics: hypothesis testing, supervised vs unsupervised learning and regression. You will not need to perform any statistical modelling or testing in the coursework.
The code portion of the coursework does not include any material from this section.
Part C) Analyse and visualize results from a health data survey
The written section of the coursework for this section must be written in RMarkdown. All other written sections are to be answered in Word documents
You will be supplied a dataset from a specific publication for the asssessment.
The code portion of the assessment wil require you to clean, wrangle and visualise data from a healthcare survey.
Part D) Going further with programming
This section is not assessed in the written or coding section of the coursework
These sections are important because they will help you understand how to apply your knowledge in the next phase of your career - or how to solve problems outside of carefully designed assessment criteria.