data-science-analysis

name	data-science-analysis
description	Computes a numeric or categorical answer to a quantitative data-science question by cleaning and analyzing local data files (CSV, Excel, TSV, and scientific formats .npz/.fits/.h5) with pandas, numpy, and scipy. Use whenever a task ships its own dataset (in whatever local directory it provides) and asks you to derive a value via statistical, geospatial, temporal, ratio, percentage, growth-rate, correlation, signal-processing, orbital, or forecasting analysis. Spans domains including archeology (demographics, paleoclimate proxies, radiocarbon dating, historical conflicts); biomedical/bioinformatics (tumor histology, genes, proteins, peptides, genomic variants); environmental (water quality, bacterial/Enterococcus exceedance, rainfall correlations, environmental justice); legal/consumer (fraud, identity theft, consumer complaints by state/metro); wildfire (incidents, causes, suppression costs, acres burned, fatalities, geospatial intersections); and astronomy/heliophysics/space weather (satellite telemetry TLE/Swarm/SP3, indices OMNI/GOES, solar activity, atmospheric density, orbital mechanics, forecasting).
version	2

Data Science Analysis

A single, domain-agnostic workflow for answering quantitative questions from local datasets. Data files may live in any directory the task provides (e.g., input/, data/, the current working directory, or an explicit path in the prompt); first identify that data directory, then apply the workflow to it. The steps below apply to any domain (archeology, biomedical, environmental, legal, wildfire, astronomy/heliophysics/space weather, and beyond) and to any file type (tabular CSV/Excel or scientific array/binary formats like .npz, .fits, .h5). Domain-specific gotchas are flagged inline and detailed in references/domain-notes.md.