Work
Analytics & BI·2025
Coffee Ratings EDA
Coffee Quality Institute dataset exploration for sourcing teams.
Role
Builder
Stack
PythonPandasSeaborn
Impact
CQI benchmark study
Problem
Sourcing teams needed clear sensory benchmarking.
Architecture
Python · Pandas · Seaborn.
Key decisions
- Pair rankings with distribution outliers.
Outcome
Actionable visuals for sourcing.
What I learned
Outliers are usually the most interesting part of the data.
What's next
Sourcing recommendation engine.
Next
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