Postgraduate Programme and Module Handbook 2021-2022 (archived)
Module MATH52315: Models and Methods for Health Data Science
Department: Mathematical Sciences
MATH52315:
Models and Methods for Health Data Science
| Type |
Tied |
Level |
5 |
Credits |
15 |
Availability |
Available in 2021/22 |
Prerequisites
Corequisites
Excluded Combination of Modules
Aims
- To introduce the knowledge and skills for the modelling and analysis of routinely collected health data
Content
- Basics of Epidemiology
- Health Economic Modelling
- Modelling techniques for discrete data
- Survival analysis
Learning Outcomes
- By the end of the module students will have a working knowledge and understanding of concepts in the following areas:
- Appreciation of different types of health data, and choice of modelling technique for a specific situation
- Measures of disease and risk
- Compartmental and agent-based models
- Decision Trees, Markov models
- Odds ratios, Logistic and Poisson regression
- Cox regression and other techniques for time-to-event data
- Students will have basic statistical skills in the following areas: modelling, simulation.
- Students will have skills in the following areas: synthesis of data and data analysis, critical and analytical thinking, statistical modelling, computer skills.
Modes of Teaching, Learning and Assessment and how these contribute to
the learning outcomes of the module
- Lectures will usually be delivered synchronously, either online or face-to-face, and will demonstrate the methodological and conceptual foundations of the modelling techniques for different types of health data. Lectures will be divided into four blocks of four lectures each, according to the classification given under the “Content” item, and other modes of teaching will be aligned accordingly. Asynchronous delivery of lectures is possible if deemed adequate by the lecturer given circumstances.
- Workshops describe theory and its application to concrete examples, concretize understanding via the application of calculational and computational methods to more complex problems, as well as providing feedback and encouraging active engagement via discussion and groupwork.
- Surgeries give students the chance to pose personalized questions on both theory and practice.
- Summative assignments are designed to test the acquisition and articulation of knowledge and critical understanding, and skills of implementation and interpretation of calculational and computational methods as applied to both synthetic and real health data-related problems.
Teaching Methods and Learning Hours
| Activity |
Number |
Frequency |
Duration |
Total/Hours |
|
| Lectures |
16 |
4 per week (Term 2, weeks 11-14) |
1 hour |
16 |
|
| Workshops (a combination of live lectures, computer practicals, problem classes, and tutorials) |
8 |
2 per week (Term 2, weeks 11-14) |
2 hours |
16 |
|
| Surgeries |
8 |
2 per week (Term 2, weeks 12-15) |
1 hour |
8 |
|
| Preparation, exercises and reading |
|
|
|
110 |
|
| Total |
|
|
|
150 |
|
Summative Assessment
| Component: Coursework |
Component Weighting: 100% |
| Element |
Length / duration |
Element Weighting |
Resit Opportunity |
| Assignment 1 (Report, essay, or task-based style) |
|
25% |
|
| Assignment 2 (Report, essay, or task-based style) |
|
25% |
|
| Assignment 3 (Report, essay, or task-based style) |
|
25% |
|
| Assignment 4 (Report, essay, or task-based style) |
|
25% |
|
<enter text as appropriate for the module>
■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University
