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2019/20 Taught Postgraduate Module Catalogue

YCHI5055M Health Data Analytics and Visualisation

15 creditsClass Size: 40

Module manager: Sam Relton
Email: S.D.Relton@leeds.ac.uk

Taught: 1 Dec to 31 Dec (1mth) View Timetable

Year running 2019/20

Pre-requisite qualifications

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

Pre-requisites

SEE ABOVE

Co-requisites

NONE

This module is mutually exclusive with

NONE

Module replaces

None

This module is not approved as an Elective

Module summary

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.

Objectives

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

Learning outcomes
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


Syllabus

- 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

Teaching methods

Due to COVID-19, teaching and assessment activities are being kept under review - see module enrolment pages for information

Delivery typeNumberLength hoursStudent hours
Class tests, exams and assessment41.004.00
Group learning18.008.00
Lecture81.008.00
Practical24.008.00
Seminar21.002.00
Tutorial101.0010.00
Private study hours110.00
Total Contact hours40.00
Total hours (100hr per 10 credits)150.00

Private study

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

Opportunities for Formative Feedback

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

Methods of assessment

Due to COVID-19, teaching and assessment activities are being kept under review - see module enrolment pages for information


Coursework
Assessment typeNotes% of formal assessment
ReportProject report on the analysis and visualisation of a given dataset100.00
Oral PresentationFormative group presentation0.00
In-course MCQFormative quizzes during the contact week with immediate feedback0.00
-------------------------Formative appraisal of published visualisation0.00
Total percentage (Assessment Coursework)100.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 25/09/2019

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