Module manager: Sam Relton
Email: S.D.Relton@leeds.ac.uk
Taught: Semester 1 Dec to 31 Dec (1mth) View Timetable
Year running 2019/20
Identical to student's parent taught postgraduate programme or PhD. Pre-requisite module: YCHI5045M Statistics for Health Sciences, or equivalent level of basic statistics knowledge
| SEE ABOVE |
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None
This module is not approved as an Elective
Data analytics skills are in increasing demand, particularly in healthcare where large amounts of data on patients and their care, and the costs of treating those patients are routinely collected. This course will provide an introduction to data analytics in healthcare, including the informative visualisation of these data. As well as highlighting the practical issues of dealing with large amounts of unstructured healthcare data, the course will cover data governance, data linkage, bias in data, new forms of data and innovations in data visualisation, including high dimensional data visualisation and the use of GIS/heatmapping to chart key issues across areas.
The purpose of the module is to provide students with a solid grounding in:
- The fundamental concepts of data analytics and data visualisation methods used in the health sciences
- Enable students to be able to apply these concepts and develop a range of strategies for interrogating complex healthcare data using data analytics and data visualisation
By the end of the module students will be able to:
- Understand data governance issues around the use of data in healthcare, including issues related to the linkage of multiple datasets from different sources
- Critically apply good data governance principles when accessing and managing healthcare data
- Understand the context of data in healthcare including being able to critically evaluate the quality of data
- Apply a range of techniques to interrogate complex healthcare data and synthesize their results constructively
- Use data visualisation principles to creatively present healthcare data
- Data governance
- Data linkage in healthcare; data storage and relational databases
- Market segmentation
- Types of data and sources of data with a particular focus on large datasets of unstructured observational data
- Bias and missing data, including the potential for these to occur and their potential impact
- Analytical methods including data mining techniques; machine learning and clustering into groups
- Data visualisation and the interplay between data analytics and data visualisation as an analytic technique
- Infographics
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Class tests, exams and assessment | 4 | 1 | 4 |
| Group learning | 1 | 8 | 8 |
| Lecture | 8 | 1 | 8 |
| Practical | 2 | 4 | 8 |
| Seminar | 2 | 1 | 2 |
| Tutorial | 10 | 1 | 10 |
| Private study hours | 110 | ||
| Total Contact hours | 40 | ||
| Total hours (100hr per 10 credits) | 150 | ||
Module pre-reading and directed exercises (12 hours)
- Basic mathematics refresher
- Background reading
During contact week (12 hours)
- Directed reading and exercises, including a critical evaluation of infographics
- Formative quizzes to consolidate learning
After contact week (86 hours)
- Summative assignment
Group feedback on directed exercises during the contact week (written)
Seminar discussions and short exercises (group feedback, verbal)
A formative peer and tutor assessed group presentation of a data visualisation on the final day of the contact week, with immediate feedback (individual feedback, verbal)
A formative assignment to be completed following the contact week, with individual written feedback before summative coursework due
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Report | Project report on the analysis and visualisation of a given dataset | 100 |
| Oral Presentation | Formative group presentation | 0 |
| In-course MCQ | Formative quizzes during the contact week with immediate feedback | 0 |
| ------------------------- | Formative appraisal of published visualisation | 0 |
| Total percentage (Assessment Coursework) | 100 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
The reading list is available from the Library website
Last updated: 25/09/2019
Errors, omissions, failed links etc should be notified to the Catalogue Team