The continual development of skills is critical to advance our research. Whether you are a student, researcher or academic, the world we live in is moving fast and methods, tools, and best practices are continually evolving. Training and skills development opportunities come in many forms: formal education, summer/winter schools, one-off courses, short-courses, longer-term programmes etc. These opportunities come with a credential status: a qualification (credential), a micro-credential (building blocks towards something more substantial) or non-credentials.

Below we have collated training and skills development opportunities from across the University of Cambridge. This list is not exhaustive and we will be adding to this when new opportunities become available. Please get in touch to add to this list.

Short-courses, one-off programmes, summer/winter schools

Accelerate Programme for Scientific Discovery

Accelerate Programme for Scientific Discovery Run a selection of short courses throughout the year.  Example opportunities include:

• Data Pipelines for Science
• Understanding Machine Learning and AI
• AI Clinic

C2D3 Data Science and Machine Learning Senior Teaching Associate

The C2D3 Data Science and Machine Learning Senior Teaching Associate designs relevant training courses through the Cambridge Centre for Research Informatics Training ( formerly Bioinformatics Training Facility) They run a wide range of courses suitable for audiences from across the whole University. More courses are being developed and delivered, so keep checking to see what is on offer. All of the courses can be found and booked through training.cam.ac.uk. Examples of the courses available:

• High Performance Computing: An Introduction
• Managing your Research Data
• An Introduction to Machine Learning
• Introduction to Statistical Analysis

Cambridge Centre for AI in Medicine

Cambridge Centre for AI in Medicine runs an annual summer school, typically online. 

• AI and Machine Learning in Healthcare Summer School

Cambridge Computational Biology Institute MPhil

The MPhil in Computational Biology course is aimed at introducing students in the biological, mathematical and physical sciences to quantitative aspects of modern biology and medicine, including bioinformatics. The course has been developed by the Cambridge Computational Biology Institute and is run by the Department of Applied Mathematics and Theoretical Physics at the Centre for Mathematical Sciences (CMS). This course is temporarily suspended.  Admissions will reopen for an October 2026 start.

Cambridge Digital Humanities

Cambridge Digital Humanities run a portfolio learning opportunities, including short courses, workshops, and data schools. Examples of previous courses include :

• CDH Methods | Machine Learning Systems: a critical introduction
• CDH Methods | Making Meaning out of Data: Machine Learning for Humanities Research
• CDH Methods | Best Practices in Coding for Digital Humanities
• Short Cultural Heritage Data School

Cambridge University Library

Cambridge University Library also offers courses relevant to data science, including

• Managing Your Research Data 
• Research Data Management

LinkedIn Learning

Through LinkedIn Learning, the University provides a number of relevant courses:

• Learning C++ (through LinkedIn Learning)
• Learning Python (through LinkedIn Learning)
• Hands-On Introduction: Python (through LinkedIn Learning)
• Machine Learning with Python
• Python Parallel and Concurrent Programming (through LinkedIn Learning)
• Learning MATLAB (through LinkedIn Learning)
• Learning Linux Shell Scripting (through LinkedIn Learning)
• R Essential Training (through LinkedIn Learning)

Research Computing Services

Research Computing Services offers courses that are freely available for all researchers at the University of Cambridge. Some examples include:

• Introduction to High Performance Computing 
• Software Carpentry for High Performance Computing
• Unix: Introduction to the Command Line Interface
• Intel oneAPI Toolkit
• Python in High Performance Computing (through Futurelearn)
• Supercomputing (through Futurelearn)
• Managing Big Data with R and Hadoop (through Futurelearn)
• Machine Learning (through Coursera)
• Deep Learning (through Coursera)

They also offer a consultancy service. To discuss your training needs, please contact support@hpc.cam.ac.uk 

Social Sciences Research Methods Programme

Social Sciences Research Methods Programme (SSRMP) provides tutor-led courses SSRMP is an interdisciplinary programme with a wide range of support for staff and postgraduate students. All of the courses can be found and booked through training.cam.ac.uk. Examples of the courses available:

• Data Visualisation Using Python
• Introduction to R
• Exploratory Data Analysis and Critiques of Significance Testing

University Information Services

University Information Services provides self-paced and tutor-led courses that can be found and booked through training.cam.ac.uk. Examples of the courses available:

• Accelerate Research with Google Cloud

Material from formal education and workshops

Teaching material from Cambridge's formal education programmes are available for all to access. Some examples are gathered below:

• Cambridge Engineering/PartIA-Computing-Michaelmas
• Machine Learning and Bayesian Inference
• Machine Visual Perception
• Natural Language Processing
• Practical Research in Human-centred AI
• Probabilistic Machine Learning 4f13
• Machine Learning and the Physical World, Advanced Data Science, Advanced topics in Machine Learning, DeepNN: Deep Neural Networks - and much more, all here provided by Professor Neil Lawrence.

Auditing courses: A comprehensive list of MPhil and PhD programmes, including CDTs and DTPs are listed on our website. Some of the programmes may be available to audit and as such their learning materials can be accessed. If this is of interest to you, you may wish to contact the programme lead.

Workshop material from previous events are available for all to access. Some examples are gathered below:

Modelling, Analysis and Inference for Digital Twins session as part of the The mathematical and statistical foundation of future data-driven engineering workshop, Isaac Newton Institute for Mathematical Sciences (2023).

External training resources

The Turing Online Learning Platform is a free and open learning research for data science and AI topics, from The Alan Turing Institute.