| Unit name | Programming and Analytics for Digital Health |
|---|---|
| Unit code | SEMTM0022 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 4 (weeks 1-24) |
| Unit director | Dr. Liu |
| 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 | School of Engineering Mathematics and Technology |
| Faculty | Faculty of Engineering |
Why is this unit important?
This unit is tailored for students from health and life science backgrounds who seek to navigate and innovate within today's rapidly evolving health and care landscape. With technology becoming increasingly integrated into healthcare, proficiency in programming and related skills on data analytics are pivotal for those seeking to use digital health to make significant improvements to patient care, medical research, and healthcare management.
Students will learn how to analyse large datasets, identify patterns, and develop predictive models tailored to individualised care. The unit empowers learners to streamline administrative tasks, optimise workflows, and standardise practices in digital health, ultimately enhancing efficiency and freeing up valuable time for more personalised patient interactions. Learning programming equips professionals in digital health with the tools to address biases, tackle health inequalities, and make informed, data-driven decisions critical for improving patient outcomes and advancing the field of healthcare.
In a rapidly digitising healthcare industry, this unit not only offers a competitive edge but also nurtures a skill set crucial for shaping the future of healthcare delivery, research, and innovation. Embracing this unit is not just an investment in personal growth but a commitment to driving positive change in healthcare.
How does this unit fit into your programme of study?
Programming and Analytics for Digital Health is designed to provide an introduction to the technological side of Digital Health. With hands-on examples of Python programming, this unit introduces students to solving real world challenges drawing on applications from epidemiology, patient data analysis, and health decision-making, including topics on data management related to healthcare data. This unit provides hands-on experience and knowledge on using Python for data manipulation, analysis, visualization, and automation in healthcare settings that will be valuable to students as they engage in projects throughout the rest of their degree.
An overview of content
Topics covered in this unit will include:
How will students, personally, be different as a result of the unit
Students will learn how to apply analytical thinking and programming techniques to navigate health data. This unit will enable them to extract insights, create meaningful visualisations and make data-driven decisions. Students will grasp the significance of data ethics in health, preparing them for roles in academia, industry, or further studies.
Learning outcomes
On successful completion of this unit, students will be able to:
1) Explain basic processes relating to data analytics and modelling in digital health.
2) Apply and use practical data science skills, applied to health and care.
3) Critique and compare data science pipelines relevant to digital health.
4) Present and interpret health data in an accessible way for diverse audiences.
Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, practical activities and self-directed exercises. The unit will be supported by regular computer labs - these will provide student-centred on-campus learning through practical problem solving and will create a supportive environment where students apply for themselves the theory and methods discussed in the unit.
Tasks which help you learn and prepare you for summative tasks (formative):
This unit will be supported by regular computer labs in which students complete tasks related to the material covered in recent lectures. Feedback on the formative exercises in the computer labs will be given by teaching staff present at the labs. Additionally, students will receive more substantial tasks related to the summative assessments that they will be encouraged to do between computer labs and they will be given opportunities to receive feedback on these tasks.
Tasks which count towards your unit mark (summative):
Individual Written Report (25%) assessing learning outcomes 1 and 3.
For this assessment, students will research an application of data analytics and modelling in digital health and write a short report that covers some technical details. The individual written report will be due at the end of TB-1.
Programming Task (75%) assessing all learning outcomes.
For this assessment, students will individually complete a programming task related to digital health and submit their code alongside some technical documentation. The programming task will be due at the end of TB-2.
When assessment does not go to plan:
Re-assessment takes the same form as the original summative assessment.
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. SEMTM0022).
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.
