Skills & training

Machine Learning and Applied AI use data-driven algorithms to model and predict outcomes, and are applied to solve complex problems and enhance real-world decision-making.

Python is a high-level, general-purpose programming language. For researchers it offers a path to automating processes and repeat tasks for reproducibility and efficiency.

SPSS is a powerful statistical software package widely used for conducting statistical analysis across multiple disciplines, including health, social sciences, and business research.

R is a widely used, open-source programming language valued across scientific fields for its strength in statistical analysis and data visualisation.

Accelerating analysis by leveraging automation, reproducible workflows or high-performance computing can extend the scale and impact of research. Learn how to make the most of command line tools, version control and parallel computing with essential research computing skills.

Bringing together biology, chemistry, computer science and statistics, bioinformatics aims to answer research questions from large and complex biological datasets.

The success of research relies on what comes before and after analysis - data and communication. Consider major factors, such as how data is captured, stored and communicated with workshops on sensitive data, research databases such as REDCap, and the psychology behind impactful communication of findings.

Supporting humanities researchers in developing technical skills relevant to qualitative research, discover training in digital tools for data collection, data handling and analysis, and data management practices that enhance research rigour and reproducibility.