Clinical Data Science / Course details

We offer our PgCert Clinical Data Science units as individual continuing professional development (CPD) courses.

These units aim 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 and translate data into patient benefit.

Our CPD units will give professionals across the board the opportunity to develop their data science skills and drive digital transformation in their practice. Participants will bring with them their clinical, health and social care knowledge and experience, and the programme will provide the computer science methods and maths, stats and machine learning skills to allow practitioners to make use of their data, adding value to their clinical work to benefit patients.

We offer the following units:

Clinical Data Engineering introduces learners to data wrangling, data quality and data governance providing them 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 the role of the data engineer/data engineering in healthcare.

Maths, Stats and Machine Learning covers 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 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 provides students 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 unit aims to:

• provide the underpinning applied mathematical concepts to common data science and machine learning methods;
• develop a working knowledge of the terminology and fundamental concepts of AI/machine learning;
• provide you with experience and practice with writing analysis scripts (using R and Python) to analyse datasets using statistical and machine learning methods;
• explore the effect and importance bias has on algorithm output;
• explore ethical and legal issues around the use of machine learning in health and clinical care;
• discuss ways that advanced statistical and machine learning models can potentially be deployed in a healthcare setting.

As busy professionals it can be hard to fit academic study around work and home life. We have designed the course to be as flexible as possible with online self-directed material, optional online synchronous sessions that are recorded so participants don't miss out if can't make all the sessions.

Course content is delivered online, enabling you to study around other commitments. We also have mandatory networking and team working events (one day for each unit, with the exception of the human factors unit that has two) and activities to help maintain motivation and build communities of practice.

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 have partnered with leading organisations in health education and care, including the National School of Healthcare Science in the National Health Service and The Christie NHS Foundation Trust.

Professional diversity

We aim to enable as widespread a representation of healthcare professional groups and roles (such as knowledge and library specialists, nurses, AHPs, healthcare scientists, doctors and beyond) as possible across the course to ensure professionals feel empowered to apply data science in practice and translate data for organisational and patient benefit.

Expert teaching

You will learn with experts that have clinical as well as industry experience working in healthcare, data science and data engineering. There will also be a variety of guest speakers from industry, academia, and healthcare.

The course is mainly delivered online, with self-directed learning materials that can be accessed at any time.

This is also supported by synchronous webinars, forums and digital communication platforms that help you to build an active learning community and benefit from networking.

There is also an optional face-to-face day allowing you to visit Manchester and make use of the university campus and equipment, as well as to meet and get your fellow students in person.

Sessions are recorded so that students who cannot make synchronous or face-to-face sessions are still able to view any sessions they miss.

You will be assessed via a coursework proposal, in which you will outline possible improvements to how existing data is utilised in a healthcare setting.

You will describe how the project will implement a form of either statistical or machine learning modelling, the nature of the data to be explored and what benefits the leveraging of the information currently embedded within the data will provide.