Skip to main content

Unit information: Data Analytics and Modelling in Health in 2020/21

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 Dr. Santos-Rodriguez
Open unit status Not open
Pre-requisites

None

Co-requisites

None

School/department Department of Engineering Mathematics
Faculty Faculty of Engineering

Description

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:

  1. Explain basic processes relating to data analytics and modelling.
  2. Apply and use practical data science skills, applied to health and care problems.
  3. Experiment with using software tools for data analytics.
  4. Illustrate the differences between different visualisation strategies.
  5. Critique and compare data science pipelines
  6. Present and interpret data to/for a non-technical audience.
  7. Be able to share data under anonymisation constraints.

Teaching details

This unit will be made up of a combination of taught lectures and Q&A sessions.

Assessment Details

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.

  • 1 x Report (100%)

Reading and References

Selected literature, references and online material will be provided at the start of the unit

Feedback