Postgraduate Course: Big data analytics (HEIN11055)
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 | Big 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 |
1) Academic 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 data in R.
2) Course outline
In this course, we introduce the fundamental principles and unique challenges of big data. The course will introduce parallel computing and its implementation for big data analytics. The course will then explore techniques and algorithms for big data analytics including traditional statistical models such as linear and logistic regression, as well as machine learning models such as neural networks.
Students will also be introduced to using sparklyr in a JupyterHub environment for big data analytics in R.
3) Student Learning Experience
Students will learn from experts in implementation science. The course is delivered online and is divided into five 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 boards will be created to give the students the opportunity to interactively and collaboratively solve problems, as well as ask questions of the instructors. Course tutors will moderate discussion boards. Students will evidence their learning by carrying out their own big data analysis by the end of the course.
Formative peer and teacher-led feedback and feedforward will be given throughout the course through the discussion boards, and summative assessment feedback and feedforward will be provided during and at the end of the course.
|
Course Delivery Information
|
| Academic year 2023/24, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Flexible |
| Course Start Date |
05/08/2023 |
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) |
This course's practical component will involve students using big data to solve real-world problems across health, social and care services settings. The assignment will consolidate learning across the five weeks and provide an opportunity for students to explore the practical challenges of handling and analysing big data. This assignment also provides students with an opportunity to gain hands-on experience in installing and using R big data analytics packages in the JupyterHub environment.
Students will be asked to select one from a set of real-world health and social care problems and related big data sets presented to students in week one of the course. Students will then design a big data analytics project to solve their selected problem over the five-week course.
Assessment summary:
LO1, LO2, LO3, LO4: Big data analytics project (80%)
LO1 and LO4: Big data analytics presentation (20%)
Summative assessment:
In Week 5, students will carry out a big data analysis on their chosen health data problem. They will create a report based on their analysis in RMarkdown, and do a 5 minute presentation communicating their results to a lay audience.
|
| 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:
- Demonstrate a critical understanding of opportunities for and challenges associated with big data analytics in health and social care settings.
- Apply specialised packages in R to solve complex health and social care problems in the big data setting
- Critically analyse big data from health and social care using computational techniques suitable for the applications under consideration.
- 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.
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 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. |
| Keywords | big data,big data analytics,JupyterHub,high performance computing,R |
Contacts
| Course organiser | Dr Kasia Banas
Tel:
Email: Kasia.Banas@ed.ac.uk |
Course secretary | Mrs Laura Miller
Tel: (0131 6)51 5575
Email: Laura.Miller@ed.ac.uk |
|
|
University Homepage