About this course

This course will equip you to influence effective decision-making regarding the use of AI within your organisation or service. Covers challenges such as data quality, ethics, governance, interoperability and stakeholder management.

About this course

Take further steps into Python libraries for Data Science (Pandas, NumPy), then combine those skills with H&SC background to build interactive online dashboard (Shiny).

About this course

Data science is revolutionising how health, social and care services are delivered. However, the use of service user data in health and social care and safe sharing of datasets within and across care systems creates ethical challenges and data ownership issues. Therefore, a balance must be struck between a desire for privacy and the need to improve the management of health and social care systems. This course introduces the ethical and data ownership issues around the use, collection, storage and utilisation of service user data from and across health, social and care service institutions.

The course develops a framework to understand the ethical issues of collecting and managing health, social and care service user data. Students will be introduced to ethical codes of practice and accountability and examine how service user data are ethically used in the health and social care setting and the impact of unethical practice. The course also covers aspects of data ownership. Key issues in data ethics and ownership, with case studies and questions, will be presented to students that prompt active self-reflection and appraisal and provide students with an opportunity to give and receive constructive feedback. Students taking this course do not need to have any prior exposure to data ethics nor ownership.

About this course

Learn about the foundations of how data is represented and how to effectively work with the types and structures of data in 2 of the core data science programming languages: Python AND R. Concepts illustrated with examples of data types and structures found in the health and social care context. No prior knowledge is necessary, but helpful to have exposure to at least one of the languages before the course.

About this course

Data visualisation, in general terms, is a tangible representation of data that provides an accessible means for analysing, understanding, and communicating the trends, outliers, and patterns of data for and across sectors and audiences. This course explores the communication of data relevant to health, social and care services settings through visualisations for exploration, analysis and related communication media. Visualisation can be a powerful means of sharing and communicating service user data to a range of audiences, such as driving policy development or improving stakeholder or broader public understanding. The course will explore theory, application, design and evaluation techniques that can be used to visualise and accessibly communicate data in various ways, such as combining vignettes taken from individual stories with data derived from official sources such as government statistics. Students will be introduced to several global projects that communicate health, social, and care services data in this way and will have the opportunity to develop their small data visualisation project.

About this course

This course introduces students to the basic theory of machine learning, whilst also helping to develop the skills necessary to implement and interpret the outputs of machine learning algorithms in Python. Course content is grounded in the context of health and social care. After completing the course, students will understand the difference between supervised (classification and regression) and unsupervised (clustering and dimensionality) techniques.

Students MUST have passed: Introduction to statistics in health and social care (HEIN11039) AND Foundations of software development in health and social care (HEIN11066) OR Data Types and Structures in Python and R (HEIN11068)

About this course

his is an introductory course in Python programming - it should be taken by students who are absolute beginners to Python and programming practices, and it will provide a solid foundation to using programming to solve issues in health and social care. Learn the philosophy and craft of creating sustainable, well-writtten code. Lots of practice in Python. Witness stories from code creators in H&SC. From complete basics all the way to analysing and visualising simple data.

About this course

This course goes beyond the basics of data-manipulation. Learn how to write lower level R code including: loops, functions, and string manipulation. Develop confidence through code review and an awareness of debugging workflows.

About this course

Demystify data science, what it is and what it is not. Introduction to core topics in data science generally and data science in the health and social care context. Build your data science toolkit with R programming for data handling/wrangling and visualisation and RMarkdown authoring framework. Apply the data science process throughout the course in the datathon assignments and build the skills and confidence to write R code from scratch. Foundation for future courses in the Data Science programme. Suitable for absolute beginners to R.

About this course

Demystify data science, what it is and what it is not. Introduction to core topics in data science generally and data science in the health and social care context. Build your data science toolkit with R programming for data handling/wrangling and visualisation and RMarkdown authoring framework. Apply the data science process throughout the course in the datathon assignments and build the skills and confidence to write R code from scratch. Foundation for future courses in the Data Science programme. Suitable for absolute beginners to R.

About this course

Demystify data science, what it is and what it is not. Introduction to core topics in data science generally and data science in the health and social care context. Build your data science toolkit with R programming for data handling/wrangling and visualisation and RMarkdown authoring framework. Apply the data science process throughout the course in the datathon assignments and build the skills and confidence to write R code from scratch. Foundation for future courses in the Data Science programme. Suitable for absolute beginners to R.

About this course

This course will teach basic statistical techniques for describing data and examining relationships between data (correlation, differences between groups, etc.). Note on Intro Stats & Data Analysis with R: If you are taking one of the above courses, you may not wish to take both. If you took the Intro to stats course and struggled a bit and would like to have another run-through of the content, with slightly more R focus, then taking the Data Analysis course is a good idea. If you are feeling (or by the end of the course do feel) really confident and wants to progress with stats, then Statistical Modelling is a good choice.

About this course

In this course, students will explore the fundamental concepts of natural language processing (NLP) and its role in current and emerging technologies. Artificial intelligence (AI) is increasingly being adopted across the health and care industry and academia, and some of the most exciting AI applications leverage NLP. Simply put, NLP is a specialised branch of AI focused on the interpretation and manipulation of human-generated spoken and written data. This course aims to develop students technical capabilities as well as a critical understanding of the ethical and social impacts of dealing with text data, covering technical aspects of the most recent and cutting-edge Natural Language Processing (NLP) technologies used in a health and care context.

About the course

Explore the fundamentals of Natural Language Processing (NLP), a key AI branch focused on interpreting and analysing human language. This course highlights NLP’s growing role in health and social care, where vast text data, from electronic health records to medical literature and social media, offers rich insights. Learn technical skills with cutting-edge NLP tools and develop a critical understanding of the ethical and social implications of handling text data. Spread across six chapters, the course combines theory and practice to prepare you for the rapidly evolving field of NLP in health, care, academia, and industry.

For more information and enrollment options, please click here

Course image by Jason Leung, 2017, Free to use under the Unsplash License

About the course

Explore further topics of Natural Language Processing (NLP), a key AI branch focused on interpreting and analysing human language. This course highlights NLP’s growing role in health and social care, where vast text data, from electronic health records to medical literature and social media, offers rich insights. Learn technical skills with cutting-edge NLP tools and develop a critical understanding of the ethical and social implications of handling text data. Spread across six chapters, the course combines theory and practice to prepare you for the rapidly evolving field of NLP in health, care, academia, and industry.

More information and enrollment options

Course image by Jason Leung, 2017, Free to use under the Unsplash License

About the course

This course provides a hands-on introduction to data science using the R programming language, tailored for health and social care practitioners. It is designed to equip learners with the fundamental skills to work with real-world data, from loading and preparing datasets to exploring, visualising, and interpreting insights relevant to health and social care.

Participants will be guided through setting up a Posit Cloud account to use RStudio and working with publicly available datasets. The course covers essential programming concepts, data manipulation techniques, and visualisation methods using ggplot2, with a strong emphasis on ethical considerations in data use. Through practical examples and case studies, learners will develop confidence in applying data science skills to analyse health and social care data effectively.

By the end of this course, participants will be able to independently explore, clean, visualise, and interpret datasets, supporting evidence-based decision-making in their professional practice.

More information and enrollment options

Course image, 2025 by Freepick