Timetable information in the Course Catalogue may be subject to change.

University Homepage
DRPS Homepage
DRPS Search
DRPS Contact
DRPS : Course Catalogue : Deanery of Molecular, Genetic and Population Health Sciences : Health Information

Postgraduate Course: Introduction to data science in health and social care (HEIN11037)

Course Outline
SchoolDeanery of Molecular, Genetic and Population Health Sciences CollegeCollege of Medicine and Veterinary Medicine
Credit level (Normal year taken)SCQF Level 11 (Postgraduate)
Course typeOnline Distance Learning AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryThis 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.
Course description 1) Academic description
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.
2) Outline Content
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 ownership and management; data handling and analysis; data visualisation and storytelling; good coding practice; implementation of evidence-based data-driven innovation, impact and implementation in the health and social care context.
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 carried out in R and RMarkdown.
Students taking this course do not need to have any prior exposure to healthcare and social service delivery systems nor data science.
3) Student Learning Experience
Students will learn from data scientists and experts from healthcare and social services. The course is delivered online and is divided into ten sessions, each lasting a week. Teaching sessions will be composed of written materials and video presentations, accompanied by guided reading in the form of links to journal articles with problem-based learning questions.
Discussion of the content and reading materials will be posted to an online forum, along with students' answers to the problem-based learning questions. Course tutors will moderate discussion boards.
Formative peer and teacher-led feedback will be given throughout the course through the discussion boards, and summative assessment feedback will be provided at the end of the course.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Students MUST NOT also be taking Health Data Science (HEIN11060)
Other requirements None
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Academic year 2023/24, Available to all students (SV1) Quota:  None
Course Start Flexible
Course Start Date 05/08/2023
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( 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 92 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %

This course's practical component will involve a data storytelling project in which students to solve real-world problems across health, social and care services settings.
This assessment will consolidate learning across the ten weeks and provide an opportunity for students to explore the practical challenges of handling health, social and care services data.

Students will choose from a set of problems from health, social and care services that can be resolved by extracting, visualising and analysing data. Data will be provided for each problem posed, and students will produce visualisations, analytics and solutions based on this data in R as well as a final report in RMarkdown.

Formal summative assessment will include a presentation and report around the student's selected topic at the end of the course. Formative feedback will be provided throughout the course through discussion board postings.
Feedback 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
Learning Outcomes
On completion of this course, the student will be able to:
  1. Demonstrate a critical awareness of health and social service systems and the key concepts, issues and methods related to data science.
  2. Effectively communicate about data-related issues in health and social services to a wide range of audiences.
  3. Apply a range of specialised data science techniques and tools to different health and social service scenarios.
Reading List
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.
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 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.
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, 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.
KeywordsNot entered
Course organiserMiss Brittany Blankinship
Course secretaryMrs Laura Miller
Tel: (0131 6)51 5575
Help & Information
Search DPTs and Courses
Degree Programmes
Browse DPTs
Humanities and Social Science
Science and Engineering
Medicine and Veterinary Medicine
Other Information
Combined Course Timetable
Important Information