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lemma-eda

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Mis à jour15 juillet 2026 à 00:47

Explore a fresh dataset when the analytical direction is open; use for orientation, pattern discovery, and deciding what analysis is worth pursuing.

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tkpratardan

tkpratardan/lemma

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Métiers associésSOC

Basé sur la classification professionnelle SOC

Scientifiques des donnéesProfessions informatiques et mathématiques·SOC 15-2051

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3 fichiers

SKILL.md

readonly

name	lemma-eda
description	Explore a fresh dataset when the analytical direction is open; use for orientation, pattern discovery, and deciding what analysis is worth pursuing.

Exploratory data analysis

Deliver

Provide a trustworthy dataset orientation, a short ranked set of findings, important limitations, and the next analytical question or baseline suggested by the evidence.

Check

Establish observation unit, source identity, coverage, candidate identifiers or targets, and a seed for sampled work.
Inspect shape, types, ranges, duplicates, missingness, time coverage, and group rates. scripts/profile_table.py is available for a deterministic first pass.
Explore only patterns that could change a decision or next step.
Investigate suspicious quality, leakage, subgroup, or temporal patterns before recommending modeling.
State a conclusion for each explored branch.

Do not generate an indiscriminate chart catalog, silently drop missing rows, infer causality, or turn a bounded question into open-ended EDA.

For visualization and chapter patterns, read references/deep-guide.md.

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Establish a dumb baseline and an honest validation harness before any real model, so every later number means something.

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lemma-causal

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Rigor for causal questions and A/B tests (the effect of acting on X): confounding, post-treatment bias, valid control groups.

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lemma-describe

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Rigor for descriptive and diagnostic analytics (what happened and why): denominators, grain, and confounded slices, not model leakage.

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lemma-eda

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EDA kickoff for a fresh dataset: fixed opening scaffold (goal, imports, load, sanity), then chapters derived from the data; scan leakage, land a baseline.

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lemma-inference

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Rigor for statistical inference (is the difference real): hypothesis tests, power, multiple comparisons, effect size over p-value.

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lemma-leakage

tkpratardan/lemma

Audit a dataset or pipeline for the five leakages that inflate a metric: target, preprocessing, temporal, group, and sampling.

2026-07-154

name	lemma-eda
description	Explore a fresh dataset when the analytical direction is open; use for orientation, pattern discovery, and deciding what analysis is worth pursuing.

Exploratory data analysis

Deliver

Provide a trustworthy dataset orientation, a short ranked set of findings, important limitations, and the next analytical question or baseline suggested by the evidence.

Check

Establish observation unit, source identity, coverage, candidate identifiers or targets, and a seed for sampled work.
Inspect shape, types, ranges, duplicates, missingness, time coverage, and group rates. scripts/profile_table.py is available for a deterministic first pass.
Explore only patterns that could change a decision or next step.
Investigate suspicious quality, leakage, subgroup, or temporal patterns before recommending modeling.
State a conclusion for each explored branch.

Do not generate an indiscriminate chart catalog, silently drop missing rows, infer causality, or turn a bounded question into open-ended EDA.

For visualization and chapter patterns, read references/deep-guide.md.