Overview
- Degree awarded
- PG Credit
- Duration
- 9 weeks (part-time)
- Entry requirements
-
We require an honours degree (minimum 2:1) or overseas equivalent or relevant work experience.
Non-standard applications for the course from applicants who have significant relevant professional experience and/or where the academic qualification falls below a 2:1 degree will be considered on an individual basis, and may be admitted at the discretion of the Programme Director.
- How to apply
Application Deadline
The deadline for applications for Human Factors and Digital Transformation (March 2024 start) and Data Visualisation and Communication (June 2024 start) is 19th January 2024.
The deadline for applications for Clinical Data Engineering (September 2024 start) and Maths, Stats and Machine Learning (November 2024 start) is 30th May 2024.
Places are limited, so you should apply early.
Applications for Human Factors and Digital Transformation (March 2025 start) and Data Visualisation and Communication (June 2025 start) will open in October 2024.
Applications for Clinical Data Engineering and Maths, Stats, and Machine Learning for 2023/24 are now closed.
How to select your CPD unit when applying
- When applying for the CPD units, you will need to select PG Cert Clinical Data Science on the application form.
- You will then see CPD as an option and you will need to select the start month for each unit.
- Please select the appropriate month for the unit you wish to apply to. For example, if applying for Clinical Data Engineering, you will need to select September; if applying for Maths, Stats, Machine Learning, you will need to select November, etc.
Unit start months:
Clinical Data Engineering – September
Maths, Stats, Machine Learning – November
Human Factors and Digital Transformation – March
Data Visualisation and Communication – June
Course options
| Full-time | Part-time | Full-time distance learning | Part-time distance learning | |
|---|---|---|---|---|
| Modular | N | Y | N | N |
Course overview
- 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.
- You will benefit from blended teaching that will provide a rich mixture of online and face-to-face learning opportunities.
- Build your digital, data capabilities and confidence.
- Develop your own data-based solutions to clinical problems.
- Learn to work better with data centric professionals on digital transformation and research projects.
- Develop fundamental data science skills for processing, analysing, and communicating using data.
Open days
Additional expenses
You should be able to complete your course without incurring additional study costs over the tuition fee. Any unavoidable additional compulsory costs totalling over 1% of the annual home UG fee per annum, regardless of whether the course is UG or PGT, will be made clear at the point of application. Further information can be found in the University's Policy on additional costs (PDF 91KB).
Policy on additional costs
All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).
Contact details
- School/Faculty
- Faculty of Biology, Medicine and Health
- Contact name
- Postgraduate Admissions Team
- Telephone
- +44 (0)161 529 4563
- pgtaught.cbm@manchester.ac.uk
- School/Faculty
-
Faculty of Biology, Medicine and Health
Courses in related subject areas
Use the links below to view lists of courses in related subject areas.
Entry requirements
Academic entry qualification overview
We require an honours degree (minimum 2:1) or overseas equivalent or relevant work experience.
Non-standard applications for the course from applicants who have significant relevant professional experience and/or where the academic qualification falls below a 2:1 degree will be considered on an individual basis, and may be admitted at the discretion of the Programme Director.
English language
International students must demonstrate English proficiency through a secure and approved testing system. We ask for English language proof if you are from non-majority English speaking countries (a list of majority English speaking countries, as defined by the UK Home Office, can be found on the GOV.UK website ). Specifically, we require a minimum of:
- IELTS: (Academic) minimum 6.5 overall with 6.0 in writing
- TOEFL: 575 paper-based
- TOEFL: 230 computer-based (with a minimum score in the Test of Written English of 6.0)
- TOEFL: 90 internet-based (with a minimum score of 22 in each component) English language test validity
English language test validity
Application and selection
How to apply
Application Deadline
The deadline for applications for Human Factors and Digital Transformation (March 2024 start) and Data Visualisation and Communication (June 2024 start) is 19th January 2024.
The deadline for applications for Clinical Data Engineering (September 2024 start) and Maths, Stats and Machine Learning (November 2024 start) is 30th May 2024.
Places are limited, so you should apply early.
Applications for Human Factors and Digital Transformation (March 2025 start) and Data Visualisation and Communication (June 2025 start) will open in October 2024.
Applications for Clinical Data Engineering and Maths, Stats, and Machine Learning for 2023/24 are now closed.
How to select your CPD unit when applying
- When applying for the CPD units, you will need to select PG Cert Clinical Data Science on the application form.
- You will then see CPD as an option and you will need to select the start month for each unit.
- Please select the appropriate month for the unit you wish to apply to. For example, if applying for Clinical Data Engineering, you will need to select September; if applying for Maths, Stats, Machine Learning, you will need to select November, etc.
Unit start months:
Clinical Data Engineering – September
Maths, Stats, Machine Learning – November
Human Factors and Digital Transformation – March
Data Visualisation and Communication – June
Advice to applicants
Your application form must be accompanied by the following supporting documents.
1. Personal statement - approximately 500 words reflecting on the following, depending on your chosen unit.
You must state on your personal statement which unit you wish to apply for.
Clinical Data Engineering:
- What is data engineering 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?
Maths, Stats, Machine Learning:
- What is Machine Learning 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?
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?
2. Full curriculum vitae (CV).
3. Degree certificate.
The deadline for applications for Human Factors and Digital Transformation and Data Visualisation and Communication is 19th January 2024.
Places are limited, so you should apply early.
Applications for Clinical Data Engineering and Maths, Stats, and Machine Learning for 23-24 are now closed.
You are expected to have access to a machine of your own to run software required for the course. We recommend a laptop/desktop machine with internet capabilities to access browser-based software.
You must state on your personal statement which unit you wish to apply for. Please see the How to Apply section for full details.
Course details
Course description
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.
Aims
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.
Special features
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.
Teaching and learning
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.
Coursework and assessment
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.
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 |
Facilities
Disability support
Careers
Career opportunities
Associated organisations
National School of Healthcare Science
National Health Service
The Christie NHS Foundation Trust