MSc Health Data Science / Course details

Year of entry: 2021

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Course unit details:
Biomedical Modelling for Health Data

Unit code IIDS67642
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 2
Offered by Division of Informatics, Imaging and Data Sciences
Available as a free choice unit? No


The health sector is rich with data that currently remains under-utilised and often uses data to look at past healthcare delivery rather than using the data in order to provide insight to enhance healthcare delivery. 

A key component skill set of a data scientist is to be able to understand and implement a suite of predictive modelling and data mining methods in order to this. 

This unit will be build on central concepts and methods introduced in the pre-requisite unit ’Fundamentals in Mathematics and Statistics in Health Data Science’ in order to provide a complete data analytical toolkit (including machine learning methods) to explore health data.

The unit will be application driven with case-studies and examples will be drawn from health research across the University of Manchester and Health eResearch Centre.


This unit aims to build on the skills introduced in the unit ‘Fundamentals in Mathematics and Statistics for Health Data Science’ to provide students with an understanding of forecasting, and more complex predictive modelling and data mining methods that allow a deeper insight into bio-health data.

The unit will develop students literacy in the strengths, characteristics and correct application of modelling techniques, and how to interpret results.

On completion students will also be able to implement analyses in an appropriate scripting language.

In addition, the unit will develop students ability to critically appraise literature that describe previously implemented methods to address healthcare problems. 

Learning outcomes

The unit will cover the following topics:

  • Section 1: Survival and Time series analysis – Kaplain Meirer; Cox (cause-specific and competing risk; time updated variables; joint longitudinal models; Survival Trees and Models; Hierarchical Linear Models;
  • Section 2: Neural Networks; Decision Trees; Ensemble-tree like methods including random forests; boosting; bagging; Kernel smoothing;
  • Section 3: Feature Selection – stepwise and shrinkage methods for linear models; Model Selection, error estimation, model robustness – in sample error estimation; extra sample error estimation.

Teaching and learning methods

The unit will be taught in a blended-learning format: basic knowledge and directed reading will be provided via eLearning so as to introduce students with key knowledge.

The face-to-face time will consist of a series of lectures and discussions in which core concepts (introduced through pre-reading) will be re-capped and any further development discussed, as well as supervised computer time during which practical software and programming problems will be explored. When possible, lectures will be recorded and distributed online.

Alongside this, two designated tutorials will be made available for students with academic staff as well as ongoing support both online and F2F.

Associated with each key concept will be a practical exercise to assess the understanding of the students.  The unit assessment will require the student to write scripts to demonstrate one or more of the techniques covered in the unit and a written report describing the justification of method; key findings and working.

Knowledge and understanding

  • Explain and discuss modelling techniques and appraise their application in healthcare.
  • Understand the strengths and weakness of modelling methods.
  • Understand the challenge of feature and model selection.

Intellectual skills

  • Critically appraise literature that uses mathematical/statistical methods for health data.
  • Assess the effectiveness and fitness fpor purpose of modelling a tool or technique.
  • Apply modelling techniques and methods to health data.
  • Interpret analytical results.

Practical skills

  • Design and write scripts to implement statistical/mathematical methods to analyse health data.

Transferable skills and personal qualities

  • Communicate and write analytical methods based on completed work and available literature in this area
  • Solve problems
  • Understand technical descriptions of statistical/mathematical analysis methods

Employability skills

Analytical skills
Understand technical descriptions of statistical/mathematical analysis methods
Problem solving
Solve problems

Assessment methods

3 x data analysis/programming assingments with written report.

 The assignment will include statistical scripts demonstrating the analysis of data and a written report (in paper style) to justify mehtods and explanation of work, comparing it to others published work.

Approx 1500 words each assignment and equal weighting between assignments.

Feedback methods

Formative assessment and feedback to students is a key feature of the on-line learning materials for this unit.  Students will be required to engage in a wide range of interactive exercises to enhance their learning and test their developing knowledge and skills. 

In addition, there will be a series of supervised practical hands-on exercises that will allow for verbal feedback. 

Study hours

Scheduled activity hours
eAssessment 30
Lectures 18
Practical classes & workshops 24
Tutorials 2
Independent study hours
Independent study 76

Teaching staff

Staff member Role
Glen Martin Unit coordinator
Nophar Geifman Unit coordinator

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