hodios-data-exploration

Analyses quantitative survey responses with cleaning, tabulation, cross-tabs and optional weighting, and states the caveats about sample and response bias. Use before reporting survey numbers.

Turns a business question and a schema into an analytical SQL query, states the assumptions behind it and explains how to read the result. Use when you know the question but not the query.

Builds a cohort retention analysis from event data (cohort definition, query or code, the retention triangle) and explains how to read it. Use to see whether newer customers stick around better.

Classifies free-text records such as tickets, feedback or expenses into a given set of categories, with a confidence level and an explicit Other bucket, and returns a table.

Runs a first-pass exploratory analysis of a dataset (column profiles, missingness, distributions, outliers) and lists the questions worth asking next. Use when you get new data.

Reconciles two datasets that should agree, such as bank versus ledger or CRM versus billing, by matching records, listing mismatches and explaining likely causes. Use for month-end checks.

Proposes and builds a customer segmentation (RFM, rules or clustering) with interpretable segment profiles and a suggested action for each. Use to target retention, pricing or marketing work.

Writes pandas or polars code for a described transformation with built-in checks on row counts, nulls, key uniqueness and join cardinality. Use when reshaping, joining or aggregating data.