| 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 combine their domain knowledge in health with skills in data analysis (statistics, machine learning) and computing / engineering to conduct complex research studies with very large datasets, sometimes with millions of individuals or thousands of variables. While the health knowledge and statistics aspects of health data science are covered by other units in the MSc programme, this unit focusses on the essential engineering skills required by data scientists at the cutting edge of health research. This includes being able to work in Linux environments, use high-performance computing services, develop reproducible pipelines, and visualise complex data.
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 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
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 two types of formative assessments. These assessments are for learning and will not contribute to the final unit mark.
The first type will support your learning by using informal questioning and group exercises in lectures and tutorials (ILOs 1-4).
The second formative assessment will be a data science 'mini project’ and involve writing an analysis pipeline to load, manipulate and visualise a health dataset using reproducible research practices. (ILOs 1-4)
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 (ILOs 1-4)
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 assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
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.
