Course description
This Clinical Data Science course aims to empower healthcare professionals from across the health and social care workforce, from Knowledge and Library Specialists to nurses, AHPs, healthcare scientists, doctors and beyond, to apply data science in practice. The course is aligned to NHS values, aiming to enhance your ability to work better together and translate data into patient benefit.
The course will support the development of data science, statistics, and machine learning capabilities across the health and social care workforce, leading to a postgraduate qualification in Clinical Data Science.
You will develop your data science skills and drive digital transformation in your practice. The course is introductory and will not train you to become a data scientist, but instead to work with data and informatics teams on digital transformation and research projects, as well as give you skills to make use of data that you may have available in your area.
You will combine your clinical, health and social care knowledge and experience with computer science methods and maths, stats and machine learning skills to make use of your data and add value to your clinical work.
Special features
Flexible learning
This course will allow you as much flexibility as possible through the use of online self-directed material and online sessions that are recorded for you to study around your other commitments. You will also benefit from networking and team working events and activities to help maintain motivation and build communities of practice.
Data science tools
You will use a learning environment that is also a data platform, giving you access and tools to work with and learn from data, including interactive digital notebooks.
Expert teaching
You will learn with experts that have clinical and industry experience working in healthcare, data science, machine learning/stats and data engineering.
Co-creation
The course has been co-designed with end users and other stakeholders (including patients) to ensure that it is of real value to working professionals in health and social care. We partnered with leading organisations in health education and care, including the National Health Service, and The Christie NHS Foundation Trust.
Teaching and learning
You will benefit from blended teaching, which is mostly online with some face-to-face learning opportunities, allowing you to study when you want around your other commitments.
There are synchronous webinars, which are recorded so you can catch up if you miss them. The majority of units have one mandatory face-to-face day of teaching (one unit has two days) in Manchester. In total there is a maximum of five days in-person delivery over the entire course.
Each of the four 15-credit units equates to around 150 hours of study. You can choose to complete all four units in one year full-time or two units per year over two years part-time.
There will also be the opportunity for networking with other digital leaders and champions, and academic experts.
This course uses real world case studies and interviews with people applying data science in healthcare, allowing you to gain a real insight into how clinical data is used in practice to benefit patients.
Coursework and assessment
Course unit details
The PGCert comprises four units of 15 credits.
Data Engineering
This unit introduces you to data wrangling and provides you with an understanding of structured and unstructured data formats, how data is modelled in various commonly used database systems, as well as an awareness of data/cyber security.
Maths, Stats and Machine Learning
You will cover data analysis methods, including statistical learning (statistics and machine learning methods) supported by knowledge and understanding of the mathematical principles underpinning these methods.
Data Visualisation and Communication
This unit focuses on the theories of visualisation and how to explore and communicate data through visualisations that can be tailored for different audiences without unintentionally misleading or confusing the intended recipient.
Human Factors and Digital Transformation
You will be provided with an overview of the process of capturing and presenting user requirements and implementing and evaluating systems in the clinical, health and social care environment.
The usual sequence of the units is:
September – Clinical Data Engineering, November – Maths, Stats and Machine Learning, February – Human Factors and Digital Transformation, May – Data Visualisation and Communication.
CPD
We are now accepting applications for Data Visualisation and Communication and Human Factors and Digital Transformation.
Your application form must be accompanied by the following supporting documents: Personal statement (approximately 500 words reflecting on the questions below that are relevant to the unit you are applying for), full curriculum vitae (CV) and degree certificate.
- Human Factors and Digital Transformation: What is human factors and digital transformation and why are you interested in learning about it in a healthcare context? How will taking this course will impact on your personal and professional development?
- Data Visualisation and Communication: What is data visualisation and communication and why are you interested in learning about it in a healthcare context? How will taking this course will impact on your personal and professional development?
Course unit list
The course unit details given below are subject to change, and are the latest example of the curriculum available on this course of study.
| Title | Code | Credit rating | Mandatory/optional |
|---|---|---|---|
| Data Engineering | IIDS69011 | 15 | Mandatory |
| Maths, Stats, and Machine Learning | IIDS69021 | 15 | Mandatory |
| Data Visualisation and Communication | IIDS69032 | 15 | Mandatory |
| Hum Factors and Digital Transformation | IIDS69042 | 15 | Mandatory |