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 equip students with the skills to analyse to big data.
This course is designed for students who 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 and basic coding experience with R is required. |
| Course description |
1) Academic description
Big data is increasingly relevant in healthcare settings due to the large volume of health data that are routinely collected across a rang of settings.
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 statistical and machine learning models in the big data setting. Students will learn how to do this using the R package sparklyr, which provides an interface into Apache Spark.
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. Lectures will teach the theoretical content of the course, while tutorials will cover practical applications.
Discussion boards will be created to give the students the opportunity to ask questions of the instructors, as well as interactively and collaboratively solve problems. Students will evidence their learning by carrying out their own big data analysis by the end of the course.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | Students must have taken Introduction to Statistics in Health and Social Care (HEIN110369) or equivalent |
Course Delivery Information
|
| Academic year 2025/26, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Flexible |
| Course Start Date |
06/04/2026 |
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 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Written exam 0%, Coursework 100%, Practical exam 0%
Assessment will include final written assignment and also incorporate other activities |
| Feedback |
Feedback is information provided to the students about their learning relative to learning outcomes. Feedback is also important to identify areas for improvement; for example, course feedback surveys will be in integral component of course development. The two main types of feedback are formative and summative. Formative feedback involves feedback given during an assessment, while summative feedback is provided after an assessment has been completed.
Feedback focuses on the student's current performance. On the other hand, Feedforward offers constructive guidance on how to do better in the future. We will use a combination of feedback and Feedforward to ensure that students achieve the four learning outcomes from this course.
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 within fifteen working days (where possible). |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate an understanding of the unique challenges associated with big data analytics in health and social care settings
- Apply specialised software that implements parallelised algorithms for analysing big data
- Appropriately carry out analysis of health data sets in a big data setting
- Demonstrate the ability to effectively communicate findings from big data analytics to audiences with varying levels of background knowledge
|
Reading List
Mastering Spark with R, Javier Luraschi, Kevin Kuo, Edgar Ruiz - https://therinspark.com/
Recommended but not essential:
R for Data Science by Hadley Wickham and Gareth Grolemund
R Markdown: The Definitive Guide by Yihui Xie, J J Allaire and Gareth Grolemund |
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.
Outlook and engagement
Students will be expected to take responsibility for their learning. This includes appropriately engaging with lectures, tutorials and discussion boards.
Skills:
Big data analytics
Students will learn how to use specialised software for big data analytics that implements various algorithms in parallel across a cluster. This will equip them with the ability to carry out big data analysis on their own.
Coding
Students will learn best practices in writing code that is clear, well-structured and efficient.
Statistics and machine learning
Students will learn basic theory underpinning selected statistics and machine learning models. |
| Keywords | big data,big data analytics,JupyterHub,high performance computing,R |
Contacts
| Course organiser | Dr Steven Kerr
Tel:
Email: steven.kerr@ed.ac.uk |
Course secretary | Miss Abbi Thomson
Tel:
Email: athoms6@ed.ac.uk |
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