Descriptive and exploratory analysis
Summaries, cross-tabulations, distributions, and data-quality profiles that show what the data contains before modelling.
Service
Research Analytics & Statistical Computing
Reproducible analytical workflows, statistical programming, and documented results for research teams.
We implement the analysis your team has designed, or help structure it, so every table and figure can be regenerated from code and data.
When teams need this
Situations this service is built for
- Results live in hand-edited spreadsheets, and the analysis cannot be re-run.
- A methods section or analysis plan needs to be implemented as working, testable code.
- Data updates arrive and every table and figure must be regenerated consistently.
- Data needs preparation before analysis can start, and the preparation itself must be documented.
- A reviewer or collaborator asks for the code behind the results.
What we can deliver
Capabilities
Analysis-ready data preparation
Cleaning, recoding, and constructing variables according to definitions your team approves.
Regression and model scripts with diagnostics
Implementation of the models your team specifies, with diagnostic checks and clear notes on assumptions.
Reproducible tables and figures
Every table and figure is produced by code from the underlying data, so updates are a re-run rather than a rebuild.
Workflow automation
Parameterized scripts or notebooks that run the whole analysis from raw inputs to final outputs.
Code review and replication packages
Review of existing analysis code and preparation of documented replication materials.
Tangible artifacts
What you receive
Deliverables are agreed in the scope. Typical artifacts for this service include:
- Analysis code in an agreed language, commonly Python
- Analysis-ready dataset with a variable dictionary
- Reproducible tables and figures
- Diagnostics and a log of assumptions and checks
- A README that explains how to re-run the analysis
Typical workflow
How the work usually runs
- 1
Discover & scope
Confirm the research question, the analysis plan your team owns, the data, and the expected outputs.
- 2
Implement
Implement data preparation and the specified analyses as scripts, starting with a small end-to-end slice.
- 3
Validate & review
Cross-check outputs, run diagnostics, and review results and assumptions with your researchers.
- 4
Document & hand over
Deliver code, outputs, and documentation so the analysis can be re-run and audited.
Rigor and constraints
Scope and methodological responsibility
- We implement and document analysis defined by your team. We do not select methods to produce a desired result.
- Advanced econometrics, causal inference, human-subject research, and other domain-specialized methods are taken on only when qualified methodological expertise is in place, either on our side or through your own methodologist. We state this at scoping.
- We do not guarantee significant findings, publication, or grant outcomes.
- We do not ghostwrite papers. Technical method documentation is part of delivery.
Who this is for
Teams we typically support with this service
FAQ
Questions about Research Analytics & Statistical Computing
Will you choose the statistical methods for us?
Method choice remains with the research team. We can discuss feasibility and implementation, and where a method needs specialist expertise we say so at scoping.
Can you guarantee significant results?
No. We implement the analysis honestly and report what the data show, including null or ambiguous results.
Do you write the paper?
No. We provide code, outputs, and technical documentation of the methods implemented. Authorship and interpretation stay with your team.
Which tools do you use?
The language and tools are agreed during scoping so that your team can maintain the work. Python is the usual starting point.
Discuss this with us
Tell us about your data and goals. The service is preselected in the inquiry form.