Postgraduate Course: Health Data Science Foundations (HEIN11088)

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	Not available to visiting students
SCQF Credits	10	ECTS Credits	5
Summary	Data science is revolutionising how medicine is understood, how biomedical research is conducted and how healthcare is delivered. Despite the widely recognised opportunities that data can bring to biomedicine and healthcare, there is a shortage of data skills and understanding of key data science concepts in the healthcare sector. This course aims to equip healthcare professionals with the key foundations and data skills that are needed for data-driven innovation. It provides an introduction to key concepts, principles and methods of data science in health, enabling students to explore the potential for data to transform healthcare. Students will learn how to use basic data analysing tools (tidyverse) and to process real-life healthcare data for effective analysis and reporting (R Markdown). They will also gain a critical understanding of the ethical and legal implications of working with healthcare data.
Course description	This foundation course aims to provide a broad and high-level introduction to data science in health, covering key concepts and principles, data analysis skills and implications of working with healthcare data. Key topics in the course include: different types of health data; data wrangling, analysis and reporting using the R programming language; legal considerations and bias in health data. This online course is based around short recorded videos, which are complemented with readings and discussions in the forums. Programming tasks in R will equip students with fundamental data skills, and online live tutorials will allow students to ask questions and discuss topics of interest. Students familiar with any programming language would not benefit from this course, as it is aimed at complete novice students in programming.

Entry Requirements (not applicable to Visiting Students)
Pre-requisites		Co-requisites
Prohibited Combinations		Other requirements	None

Course Delivery Information

Academic year 2024/25, Not available to visiting students (SS1)		Quota: 1
Course Start	Flexible
Course Start Date	16/09/2024
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 )
Assessment (Further Info)	Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment)	Written Exam 0%, Coursework 100%, Practical Exam 0%
Feedback	Formative feedback will be provided throughout the course, for example, during live question and answer sessions, tutorials, quizzes, and discussion boards. Summative feedback provides an evaluation of how much a student has learned at the end of the course through a final assessment.
No Exam Information

Academic year 2024/25, Not available to visiting students (SS2)		Quota: None
Course Start	Flexible
Course Start Date	06/01/2025
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 )
Assessment (Further Info)	Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment)	Written Exam 0%, Coursework 100%, Practical Exam 0%
Feedback	Formative feedback will be provided throughout the course, for example, during live question and answer sessions, tutorials, quizzes, and discussion boards. Summative feedback provides an evaluation of how much a student has learned at the end of the course through a final assessment.
No Exam Information

Academic year 2024/25, Not available to visiting students (SS3)		Quota: 100
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 )
Assessment (Further Info)	Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment)	Written Exam 0%, Coursework 100%, Practical Exam 0%
Feedback	Formative feedback will be provided throughout the course, for example, during live question and answer sessions, tutorials, quizzes, and discussion boards. Summative feedback provides an evaluation of how much a student has learned at the end of the course through a final assessment.
No Exam Information

Learning Outcomes
On completion of this course, the student will be able to: Explain and critically discuss key concepts, principles and methods of data science in health. Analyse health data with the use of the R programming language, including summarisation, visualisation and interpretation. Critically examine the ethical, societal and regulatory principles and implications of data science in health.

Learning Resources
There is no compulsory course text. Pointers to appropriate material from different freely-available resources will be made available online, including the electronic version of the HealthyR textbook.

Additional Information
Graduate Attributes and Skills	By the end of the course, students should have strengthened their skills in: Communication, including communicating complex ideas and arguments to a range of audiences with different levels of knowledge/expertise. Digital literacy and numeracy, including using advanced data analysis tools to support their research and enquiry. Critical and analytical thinking, including applying critical analysis, synthesis and evaluation to key approaches and development in the subject. Personal and intellectual autonomy, including planning organising work, time management and taking responsibility for own work.
Keywords	data science,healthcare data,R programming,ethics,data-driven innovation,foundations

Contacts
Course organiser	Mr Dimitrios Doudesis Tel: Email: Dimitrios.Doudesis@ed.ac.uk	Course secretary	Ms Rebecca Sewell Tel: (0131 6)51 7112 Email: Rebecca.Sewell@ed.ac.uk