Data Analysis & Data Science with R
From raw data to defensible evidence: preparation, statistical analysis, modelling and decision-oriented outputs built for the people who use them.
The hard part is rarely the model. It is making the entire analytical chain trustworthy.
Poorly defined metrics, inconsistent data preparation and undocumented assumptions can undermine even sophisticated models. We focus on traceability, reproducibility and the link between analytical output and the decision it supports.
A defined scope. Tangible deliverables.
Data quality and preparation workflows
Exploratory and statistical analysis
Predictive or explanatory models where appropriate
Validation and sensitivity checks
Decision-ready visualisations and reports
Reproducible analytical code and documentation
Make the technical decisions explicit.
- Clarify the decision and success criteria first
- Audit data quality before modelling
- Use methods proportionate to the question
- Validate assumptions and edge cases
- Deliver outputs that non-specialists can act on
Outcomes to define and assess together
- Clearer decisions grounded in transparent analysis
- Repeatable workflows instead of one-off spreadsheets
- Better communication between data and business teams
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.
Connecting analysis with a specific decision
A team needs to understand changes in an activity. Start with indicator definitions, data coverage and potential bias. Select a method that supports a clear explanation of results, assumptions and limitations.
A reproducible workflow, documented analysis and a decision-oriented presentation.
Illustrative scenario, not a claim about a completed client engagement. Final deliverables depend on the agreed scope.
A useful first conversation.
Which decision needs evidence? What data is available and authorised? How will usefulness be assessed?
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.