| Unit name | Applied Health Data Science |
|---|---|
| Unit code | BRMSM0057 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Dr. Davis |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
None |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | Bristol Medical School |
| Faculty | Faculty of Health Sciences |
Why is this unit important?
Health data scientists and medical statisticians often conduct complex research studies with very large datasets, sometimes with millions of individuals or thousands of variables. To carry out these studies effectively, researchers need key data science skills such as being able to work in Linux environments, use high-performance computing services, develop reproducible pipelines, and visualise complex data . This unit provides you with these essential skills used by data scientists at the cutting edge of health research.
How does this unit fit into your programme of study
This unit is designed specifically for students on the MSc in Medical Statistics and Health Data Science. While particular analytical approaches such as regression analyses and machine learning are taught in other units, this unit provides practical data science skills and knowledge in different types of health data that can be applied throughout the rest of the programme to help you work effectively and appropriately with very large health datasets.
An overview of the content
This unit aims to introduce you to the use of ‘big data’ in healthcare including:
How will students, personally, be different as a result of the unit
You will have the practical skills to work with very large health datasets using reproducible research practices, and be equipped to consider ethical issues to appropriately manage and work with participant information.
Learning Outcomes
On successful completion of the unit, you should be able to:
The learning of health data science approaches is most effective when it is practice-based. Teaching will include learning activities such as lectures introducing theoretical concepts, individual tasks and practicals to apply what you have learnt, discussions around important issues, and small group work that reflects the way data science is often practised in team science environments. Directed and self-directed learning will include activities such as reading, accessing web-based supplementary materials, critical analysis, and completion of assessments.
Tasks which help you learn and prepare you for summative tasks (formative):
There will be three types of formative assessments. The first type will support your learning by using informal questioning and group exercises in lectures and tutorials. These assessments are for learning and will not contribute to the final unit mark (ILOs 1-5)).
The second formative assessment will be a group exercise where you will be given a research proposal and will need to write a data management plan to accompany it. You will be provided with a model answer to the exercise and will be asked to carry out peer-marking in groups. (ILOs 1,2,5).
The third formative assessment will be a ‘data challenge’ and involve writing an analysis pipeline to load, manipulate and visualise a health dataset using reproducible research practices. (ILOs 3-5)
Tasks which count towards your unit mark (summative):
The summative assessment will consist of one piece of coursework.
The coursework will involve writing an analysis pipeline to load, manipulate and visualise a health dataset using reproducible research practices. Before implementing the pipeline you will be asked to consider how the data will be managed and stored, how the data will be handled and protected during and after completion of a project, and the potential for data sharing and access. (ILOs 1-5)
When assessment does not go to plan
If you do not pass the unit, you will normally be given the opportunity to take a reassessment as per the Regulations and Code of Practice for Taught Programmes. Decisions on the award of reassessment will normally be taken after all taught units of the year have been completed. Reassessment will normally be in a similar format to the original assessment that has been failed.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. BRMSM0057).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
