Consumer Data Analysis
Budget: $2 – $8 USD
I have a sizeable set of consumer-level data that needs to be examined with a clear, methodical approach. My priority is rigorous data analysis that turns raw records into well-supported insights I can act on right away.
What I will provide
• A cleaned CSV export and the associated data dictionary.
• Context on how the data was collected and any known limitations.
What I need from you
• Exploratory and statistical analysis that highlights key patterns, correlations, and anomalies.
• A concise written summary (PDF or Google Doc) explaining findings in plain English.
• A set of reproducible notebooks or scripts (Python, R, or SQL—whatever you prefer) so I can rerun the analysis later.
• Optional but welcome: clear, quick visual snapshots (charts or dashboards) that make the results easy to present internally.
Acceptance criteria
1. All code is fully commented and runs end-to-end on the supplied dataset.
2. Insights are backed by numbers and clearly referenced to the source fields.
3. Final deliverables arrive in an agreed-upon shared folder and open without errors.
Tools you are comfortable with—Pandas, NumPy, R tidyverse, Looker Studio, Tableau—let me know; I’m flexible as long as the outcome is solid, transparent, and repeatable.
What I will provide
• A cleaned CSV export and the associated data dictionary.
• Context on how the data was collected and any known limitations.
What I need from you
• Exploratory and statistical analysis that highlights key patterns, correlations, and anomalies.
• A concise written summary (PDF or Google Doc) explaining findings in plain English.
• A set of reproducible notebooks or scripts (Python, R, or SQL—whatever you prefer) so I can rerun the analysis later.
• Optional but welcome: clear, quick visual snapshots (charts or dashboards) that make the results easy to present internally.
Acceptance criteria
1. All code is fully commented and runs end-to-end on the supplied dataset.
2. Insights are backed by numbers and clearly referenced to the source fields.
3. Final deliverables arrive in an agreed-upon shared folder and open without errors.
Tools you are comfortable with—Pandas, NumPy, R tidyverse, Looker Studio, Tableau—let me know; I’m flexible as long as the outcome is solid, transparent, and repeatable.