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DRPS : Course Catalogue : Deanery of Molecular, Genetic and Population Health Sciences : Health Information

Postgraduate Course: Big data analytics (HEIN11055)

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 AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryBig data refers to the use of data science techniques to capture, share, manage and analyse vast and complex datasets. Big data is challenging in health and social care not only because of its volume, but also because of the diversity of data types and the speed at which data is generated. There are many applications for big data in health and social care including genomics, imaging, Internet of things (IoT) and wearables, and population studies.

This course aims to introduce students to the fundamental principles of big data and how big data is being applied in health and social care, and equip students with the knowledge appreciation of the challenges associated with and the and skills to analyse to big data.

This course is designed for students who are interested in big data analytics and wish to understand how to analyse big data using R programming language in the health and social care context. Prior knowledge of basic statistics concepts, data handling and analysis with R and best data management and coding practice is required.
Course description Big data in health, social and care services is a term used to describe the large volumes of data created by the adoption of digital technologies that collect service users and administrative records that are used in the management of care systems. Big data in health, social and care services is used for improving service user care and wellbeing, and the management of care systems, care services and data security. Big data challenges include data ethics, capture, storage, analysis, sharing, and visualisation and information privacy.

JupyterHub is an environment to store and manage, and analyse big data in R.

In this course, we introduce the fundamental principles of big data, opportunities for big data in health and social care, and the challenges associated with the ethics, capture, mining, storage, sharing and analysis of big data. The course will then cover the various techniques and systems for big data analytics. Next the course focus big data techniques in the health, social and care service analytic applications such as predictive modelling, dimensionality reduction, network analysis, and computational phenotypes derived from neural and behavioural data and service user similarity analysis from care records and genetic data. Finally, the course will explore big data analytics: scalable machine learning algorithms such as deep learning and artificial intelligence in person-centred care and big data analytic systems such JupyterHub and high-performance computing (HPC).

Students will also be introduced to Linux and bash shell scripting and accessing virtual environments and get hands-on experience with JupyterHub for big data analytics in R using HPC facilities at The University of Edinburgh.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2022/23, Not available to visiting students (SS1) Quota:  None
Course Start Flexible
Course Start Date 31/10/2022
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 A balance of formative feedback and feedforward will be provided throughout the course, for example, during live question and answer sessions and on discussion boards. Formative tasks will be offered before the student submit their summative assessed coursework. All components of summative assessment will be marked, and feedback will be provided.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Demonstrate a critical understanding of opportunities for and challenges associated with big data analytics in health and social care settings.
  2. Apply a range of specialised R analytical packages within the JupyterHub environment and high-performance computing to solve complex health and social care problems.
  3. Critically analyse big data from health and social care using computational techniques suitable for the applications under consideration.
  4. Demonstrate the ability to effectively communicate findings from big data analytics with peers and a wide range of audiences within the health and social care sector.
Reading List
A reading list will be provided on the course virtual learning environment.
Additional Information
Graduate Attributes and Skills Mindsets:
Enquiry and lifelong learning
Students on this course will be encouraged to seek out ways to develop their big data analytic expertise. They will also be encouraged to strive for excellence in their professional practice and to use established and developed approaches to use big data to resolve complex issues as they arise in their practice.

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 they wish to develop and grow. Students will also be encouraged to understand their responsibility within and contribute positively, ethically and respectfully to the academic 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 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.

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.

Effective big data analytics requires 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.
Keywordsbig data,big data analytics,JupyterHub,high performance computing,R
Course organiserMiss Michelle Evans
Tel: (0131 6)51 5440.
Course secretaryMrs Laura Miller
Tel: (0131 6)51 5575
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