Tag: data science

A fixture-backed comparison that holds the BLS source, transformations, K-means settings, and acceptance criteria constant, then asks what Elixir and Python make easier to inspect and trust.

Reader outcome: Reader can design a cross-stack experiment that tests output agreement without turning a small analytical workload into a misleading language benchmark.

A software-developer guide to the statistics that actually change data-science decisions: samples, estimates, uncertainty, effect size, bias, probability, distributions, and model metrics.

Reader outcome: Defined a practical estimate-review workflow that helps software developers report effect size, confidence intervals, p-values, sampling bias, and classification metrics without treating statistics as glossary trivia.