About Digital Skills
About the Digital Skills Programme
Practical, reusable training for researchers who need reliable digital methods.
Learn Skills You Can Use Immediately
The Digital Skills programme is built around practical research workflows, not abstract tool demos. Each course gives learners something they can reuse: a cleaner folder structure, a safer data process, a working automation, a reproducible analysis, or a clearer AI workflow.
๐งญ Find the Right Starting Point
Choose foundation, responsible AI, automation, Python, or R depending on your current work.
๐ ๏ธ Build While You Learn
Sessions combine short teaching blocks with practical activities and discussion.
๐ Reuse the Materials
Slides, setup guides, examples, and references are available from each course page.
Why This Programme Exists
Modern research projects routinely involve code, large data sets, digital collaboration, and complex computational workflows. Many researchers have limited time to build confidence with software engineering, digital infrastructure, automation, or best practice for managing data.
The programme addresses this gap through a structured training pathway aligned with open science, reproducible research, and research integrity.
Structured Progression
Courses are organised so learners can build skills incrementally and revisit concepts as needed.
Hands-On Learning
Sessions use collaborative exercises, scenario-based tasks, and practical build activities.
Open Science Focus
Modules emphasise FAIR data, reproducible workflows, responsible governance, and transparent documentation.
Discipline-Agnostic Design
The methods and tools apply across research domains, with space for specialist workflows where needed.
Who It Is For
The programme supports learners at different levels of experience, from researchers developing foundational digital confidence to advanced learners who want to strengthen computational, analytical, and data-driven workflows.
Doctoral Researchers
Build practical habits for data, automation, analysis, and responsible use of AI.
Early-Career Researchers
Strengthen workflows that can be reused across projects, publications, and collaborations.
Analysts and Professional Services
Improve repeatable digital processes, reporting workflows, and operational data handling.
Training Tiers
Learners can move through the pathway in order or choose the course that matches the work in front of them.
Foundation
Essential Digital Skills introduces the core tools, policies, and principles required for computational research.
Extend
Responsible AI and automation courses help learners apply practical guardrails to common research and business processes.
Analyse
Python and R courses support reproducible data analysis, modelling, reporting, and debugging.
Delivery Model
Sessions are primarily in person to support discussion, collaboration, and peer learning. Cohorts are cross-disciplinary where possible so learners can compare workflows across different research contexts.
Assessment is formative. Learners receive feedback from instructors, peers, and the practical behaviour of the systems they build.
Materials Usually Include
- Slides
- Learner setup guides
- Practical exercises
- Example data or documents
- Reference pages
- Instructor notes
Contact
For general enquiries, registration, or accessibility queries, contact the Digital Research Service.