Corporate R Training & Project Mentoring
Custom workshops and training programmes designed from your datasets, workflows and operating constraints — with immediate application back on the job.
Generic training transfers information. Effective training changes how teams work.
Teams learn faster when examples, exercises and standards mirror their own environment. We build programmes that connect technical skills with production practices, business context and the tools participants actually use.
A defined scope. Tangible deliverables.
Training needs assessment
Custom programme and practical exercises
R and data science workshops
Shiny and Posit training
SAS-to-R migration enablement
Generative AI for Data and business teams
Follow-up mentoring and office hours
Make the technical decisions explicit.
- Start from your use cases rather than a fixed catalogue
- Balance concepts with hands-on exercises
- Use your tooling and, where appropriate, your own data
- Teach quality, testing and reproducibility alongside syntax
- Measure autonomy after the course, not attendance alone
Outcomes to define and assess together
- Faster adoption of new tools and standards
- More autonomous Data and Analytics teams
- Training that translates directly into operational productivity
A few useful starting points.
Can you work on an existing project?
Yes. We begin by reviewing its purpose, dependencies, documentation and current constraints. This assessment determines what can be retained and what needs to change.
What should we prepare for the first discussion?
The expected outcome, current tools, main difficulties and any deadline. An architecture sketch or a description of the workflow is enough to start; sensitive data is not needed at this stage.
How are scope, timing and price set?
After reviewing the context, we define the intervention, deliverables, dependencies and acceptance criteria. Timing and pricing follow from this scope and are confirmed in the proposal.
Training around the work your team performs
A team knows basic R and wants to structure its analytical work more effectively. Connect functions, tests, packages and collaboration practices, then assess learning through a defined practical task.
A tailored programme, materials, exercises and a learning review.
Illustrative scenario, not a claim about a completed client engagement. Final deliverables depend on the agreed scope.
A useful first conversation.
What is the starting level? Which tasks must the team master? How much practice time is available?
Share a description of the workflow and its constraints first. Confidential files, personal data and system access can be discussed within an appropriate framework later.