Please note: Due to alternative arrangements for teaching and
assessment in place from 18 March 2020 to mitigate against the restrictions in
place due to COVID-19, information shown for 2019/20 may not always be accurate.
Please note: you are viewing unit and programme information
for a past academic year. Please see the current academic year for up to date information.
| Unit name |
Data Analytics and Modelling in Health |
| Unit code |
EMATM0046 |
| Credit points |
10 |
| Level of study |
M/7
|
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24)
|
| Unit director |
Professor. Santos-Rodriguez |
| Open unit status |
Not open |
| Pre-requisites |
None
|
| Co-requisites |
None
|
| School/department |
School of Engineering Mathematics and Technology |
| Faculty |
Faculty of Engineering |
Description including Unit Aims
The aim of this unit is to allow students to acquire fundamental skills covering the full data science pipeline, including pre-processing, manipulation, integration, storage, modelling, exploration, visualisation and privacy.
Unit content:
- Data ingress and pre-processing.
- Data storage and data management
- Data transformation and integration.
- Data exploration, modelling and visualization.
- An introduction to concepts of data sharing, privacy and anonymization.
Intended Learning Outcomes
Having completed this unit, the student is expected to:
- Explain basic processes relating to data analytics and modelling.
- Apply and use practical data science skills, applied to health and care problems.
- Experiment with using software tools for data analytics.
- Illustrate the differences between different visualisation strategies.
- Critique and compare data science pipelines
- Present and interpret data to/for a non-technical audience.
- Be able to share data under anonymisation constraints.
Teaching Information
This unit will be made up of a combination of taught lectures and Q&A sessions.
Assessment Information
Group project suggested by health care stakeholders that demonstrates the full data analysis pipeline for a particular dataset, including a discussion of possible alternative approaches at each step.
- Presentation (10%)
- Report (5,000 words) (90%)
Reading and References
Selected literature, references and online material will be provided at the start of the unit
