Postgraduate Course: Applied Software Development in Health and Social Care (HEIN11062)
Course Outline
| School | Deanery of Molecular, Genetic and Population Health Sciences |
College | College of Medicine and Veterinary Medicine |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | Python is among the most popular programming languages for handling data. Increasingly it is being used to build innovative data-driven health and social care solutions for improvement of health and wellbeing and enhancement of care services delivery. This course builds on previous courses in Python programming and will advance students skills in: handling datasets, using data libraries, visualisation and building interactive dashboards. These will be used to tackle challenges in data science in the health and social care sector. This course is ideal for students from health, social and care services or from computational backgrounds who have had previous structured experience of Python who would like to build on it and learn to apply Python programming in the health and social care context. Intermediate knowledge of Python programming is required.
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| Course description |
Using programming to analyse data is an essential aspect of health, social and care service delivery. Care providers need to collect, manage, handle, and analyse administrative and service user data to improve health and wellbeing and drive service delivery improvements. This course provides next steps for students who already mastered the foundations of Python. Course will emphasise working with files and online data sources (APIs) to enable exchange of information and integration across systems. This course is designed to equip students with further Python programming skills to write clear, reliable, efficient, and reusable code. Data science libraries will be used to manipulate, collect, explore, visualise, analyse, publish, and extract value from health and social care data. The course will provide students with further hands-on Python language experience to answer health and social care-related questions from the outset of the course. Data Science libraries (such as NumPy and Panda) will be used to work with data acquired from publicly available sources and databases. Students will learn how to import, handle, visualise and analyse data.
The course is delivered online over 5 weeks. Each week will include self-directed learning materials and practical online live sessions.
Self-directed learning materials (videos and practical programming exercises) introduce programming concepts. These new skills and knowledge are then used during practical pair-programming live sessions. During live sessions students are connected into pairs and work together to solve programming puzzles.
Students solve practical challenges using health and social care data, as they build their skills. Over the 5 weeks students prepare for their final assignment, in which they will have to produce an interactive dashboard and a mini-report related to a real-life question.
Formative peer and teacher-led feedback will be given throughout the course during live coding sessions, through the discussion boards, and summative assessment feedback will be provided at the end of the course.
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Information for Visiting Students
| Pre-requisites | Foundations of Software Development in Health and Social Care or equivalent |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2024/25, Available to all students (SV1)
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Quota: None |
| Course Start |
Flexible |
| Course Start Date |
07/04/2025 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 5,
Seminar/Tutorial Hours 1,
Online Activities 35,
Feedback/Feedforward Hours 5,
Formative Assessment Hours 5,
Revision Session Hours 1,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
46 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Written Exam 0 %, Coursework 100 %, Practical Exam 0 % |
| Feedback |
Formative peer and teacher-led feedback will be given throughout the course during live coding sessions, through the discussion boards, and a formative task will also be offered before the student submitting their summative assessed course work.
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| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate a critical understanding of the Python language and software ecosystem, its applications in collecting, curating, and analysing health, social, and care services data.
- Apply the Python programming language and libraries for data handling and data analysis to write functional, readable and reusable source code
- Apply logical, analytical, and problem-solving skills to review and critically evaluate Python programs.
- Make appropriate use of data visualisation tools, user interfaces and publication methods, to effectively communicate the results of data analysis to both technical and non-technical audiences.
- Collaborate with others on their code, be able to ask and answer questions about it. Critically reflect on their own and others' roles in collaborative teams and take responsibility for their work and others' work.
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Additional Information
| Graduate Attributes and Skills |
1) Mindsets:
Enquiry and lifelong learning
Students on this course will be encouraged to seek out ways to develop their expertise in software development. They will also be encouraged to strive for excellence in their professional practice and to use established and developed approaches to resolve software related issues as they arise in health and social care systems.
Aspiration and personal development
Students will be encouraged to draw on the quality, depth and breadth of their experiences to expand their potential and identify areas in which they wish to develop and grow. Students will also be encouraged to understand their responsibility within, and contribute positively, ethically and respectfully to the health and social care community, while acknowledging that different students and community members will have other priorities and goals.
Outlook and engagement
Students will be expected to take responsibility for their learning. Students will be asked to use on their initiative and experience, often explicitly relating to their professional, educational, geographical or cultural context to engage with and enhance the learning of students from the diverse communities on the programme. Students will also be asked to reflect on the experience of their peers and identify opportunities to enhance their learning.
2) Skills:
Research and Enquiry
Students will use self-reflection to seek out learning opportunities. Students will also use the newly acquired knowledge and critical assessment to identify and creatively tackle problems and assimilate the findings of primary research and peer knowledge in their arguments, discussions and assessments.
Personal and intellectual autonomy
Students will be encouraged to use their personal and intellectual autonomy to critically evaluate learning materials and exercises. Students will be supported through their active participation in self-directed learning, discussion boards and collaborative activities to critically evaluate concepts, evidence and experiences of peers and superiors from an open-minded and reasoned perspective.
Personal effectiveness
Students will need to be effective and proactive learners that can articulate what they have learned, and have an awareness of their strengths and limitations, and a commitment to learning and reflection to complete this course successfully.
Communication
Effective data scientists' practitioners in the health and social care sector require excellent oral and written communication, presentation and interpersonal skills. The structure of the interactive (problem-based learning examples, discussion boards and collaborative activities) and assessment elements incorporate constant reinforcement and development of these skills.
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| Keywords | Python,Data,Software,API,Database,Programming |
Contacts
| Course organiser | Dr Pawel Orzechowski
Tel:
Email: porzecho@ed.ac.uk |
Course secretary | Mrs Laura Miller
Tel: (0131 6)51 5575
Email: Laura.Miller@ed.ac.uk |
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