Data Analysis Research Developer Needed
Budget: $15 – $25 USD
My current research project has reached the point where raw numbers must be turned into clear, decision-ready insights. Your main objective is to take the datasets I provide (CSV and SQL exports) and run rigorous data analysis that uncovers patterns, correlations, and actionable findings.
The scope centres on:
• Cleaning and transforming the data so every variable is usable and well-labeled
• Running descriptive and inferential statistics in Python, R, or another tool you are comfortable with
• Visualising key results in charts or dashboards that a non-technical stakeholder can understand
• Summarising methods, assumptions, and conclusions in a concise report (Word or PDF)
I have no strict preference on the statistical package as long as the workflow is reproducible—Jupyter notebooks, R Markdown, or even a well-commented spreadsheet model are all acceptable. If you feel a short literature review or a lightweight survey design would strengthen the analysis, mention it in your proposal; I’m open to expanding the scope, but strong data analysis is the must-have skill.
Deliverables are accepted once I can:
1. Re-run your code and obtain identical outputs.
2. Follow your documentation to trace each step from raw data to final insight.
3. Share the visualisations with stakeholders without further editing.
Timeline is flexible within reason; quality and clarity outweigh speed. Let me know what tools you plan to use, the statistical techniques you foresee applying, and any clarifying questions you have about the data itself so we can get started right away.
The scope centres on:
• Cleaning and transforming the data so every variable is usable and well-labeled
• Running descriptive and inferential statistics in Python, R, or another tool you are comfortable with
• Visualising key results in charts or dashboards that a non-technical stakeholder can understand
• Summarising methods, assumptions, and conclusions in a concise report (Word or PDF)
I have no strict preference on the statistical package as long as the workflow is reproducible—Jupyter notebooks, R Markdown, or even a well-commented spreadsheet model are all acceptable. If you feel a short literature review or a lightweight survey design would strengthen the analysis, mention it in your proposal; I’m open to expanding the scope, but strong data analysis is the must-have skill.
Deliverables are accepted once I can:
1. Re-run your code and obtain identical outputs.
2. Follow your documentation to trace each step from raw data to final insight.
3. Share the visualisations with stakeholders without further editing.
Timeline is flexible within reason; quality and clarity outweigh speed. Let me know what tools you plan to use, the statistical techniques you foresee applying, and any clarifying questions you have about the data itself so we can get started right away.