Postgraduate Course: Introduction to data science in health and social care (HEIN11037)
|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
|Summary||This unique postgraduate course brings together a wide range of learners with a passion for data-driven innovation across health, social and care services. Students will gain a critical understanding of the current and emerging issues, key concepts and methods in data science used to improve health and wellbeing, and the management of systems in healthcare and social services.
This course will also provide students with an opportunity to demonstrate originality and creativity in the application of tools from data science.
By the end of this course, students will have a critical understanding of the current issues, key concepts and methods in data science used to improve health and wellbeing and management of systems in the health, social and care services sector.
Data science is revolutionising how health, care and social services are being delivered. Despite the widely-recognised opportunities that data can bring to service delivery, there is a shortage of data skills in the health, care and social services sector. This course aims to equip health and social care professionals with the fundamental foundations and data skills that are needed for data-driven innovation. It provides an introduction to critical concepts, health and social care systems, systems thinking, principles and methods of data science, enabling students to explore the potential for data to transform healthcare and social services delivery. Students will learn how to and gain practical experience in data science tools to process service user data for effective analysis and reporting. They will also gain a critical understanding of ethical and legal implications of working with healthcare and social services data shaping their practice and ensuring optimal health, social and care service provision.
This course will introduce students to health and social care delivery and organisation; the tools and algorithms used in data science in the health and social care context, data management; data handling and analysis; implementation of evidence-based data-driven innovation, health and social care economics, and entrepreneurship and innovation in health and social care.
This course will also provide students with practical hands-on experience of a variety of situations that arise in the analysis of service user and administrative data from healthcare and social services through worked examples and a group 'datathon' project carried out in R.
Students taking this course do not need to have any prior exposure to healthcare and social service delivery systems nor data science.
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| None
Information for Visiting Students
Course Delivery Information
|Academic year 2021/22, Available to all students (SV1)
|Course Start Date
|Learning and Teaching activities (Further Info)
Lecture Hours 10,
Seminar/Tutorial Hours 2,
Online Activities 70,
Feedback/Feedforward Hours 10,
Formative Assessment Hours 10,
Revision Session Hours 2,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
||Feedback is information provided to the students about their learning relative to learning outcomes. The two main types of feedback are formative and summative. Formative feedback is generated to engage learners to constantly reflect on how they can approach, orient and evaluate learning, which leads to successful learning outcomes. Summative feedback provides an evaluation of how much a student has learned at the end of the course through a final assessment.
Formative feedback will be provided throughout the course, for example, during live question and answer sessions, quizzes, and discussion boards. A formative task will also be offered before the student submitting their summative assessed course work. All assignments will be marked, and feedback is provided within fifteen working days (where possible).
|No Exam Information
On completion of this course, the student will be able to:
- Demonstrate a critical awareness of health and social service systems and the key concepts, issues and methods related to data science.
- Effectively communicate about data-related issues in health and social services to a wide range of audiences.
- Apply a range of specialised data science techniques and tools to different health and social service scenarios.
|Recommended, but not essential:|
M.J. Crawley (2013) The R Book.
C. O'Neil and R. Schutt (2013) Doing data science.
S. Consoli, D. Reforgiato Recupero and M. Petkovi¿ (2019) Data Science for Healthcare: Methodologies and Applications.
L.A. Celi, M.S. Majumder, P. Ordóñez, J. S. Osorio, K.E. Paik and M. Somai. (2020) Leveraging Data Science for Global Health.
S.R. Deeny and A. Steventon (2015) Making sense of the shadows: priorities for creating a learning healthcare system based on routinely collected data. BMJ Quality and Safety 24:505-515.
G.M. Clarke, S. Conti, A.T. Wolters and A Steventon (2019) Evaluating the impact of healthcare interventions using routine data. BMJ 365: l2239.
|Graduate Attributes and Skills
Enquiry and lifelong learning
Students on this course will be encouraged to seek out ways to develop their expertise in data science in health and social care. They will also be encouraged to strive for excellence in their professional practice and to use established and developed approaches to resolve ethical challenges and data ownership issues as they arise in health and social care systems. Students will be expected to show both depth and breadth of enquiry to fully understand, value and bound problems across the whole health and social care system.
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, value and creatively tackle problems and assimilate the findings of primary research and peer knowledge in their arguments, discussions and assessments.
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.
|Course organiser||Dr Mairead Bermingham
|Course secretary||Miss Magdalena Mazurczak