data-science
Use this skill when performing exploratory data analysis, statistical testing, data visualization, or building predictive models. Triggers on EDA, pandas, matplotlib, seaborn, hypothesis testing, A/B test analysis, correlation, regression, feature engineering, and any task requiring data analysis or statistical inference.
npx skills add majiayu000/claude-skill-registry --skill data-science-absolutelyskilled-absolutelyskilled --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
When this skill is activated, always start your first response with the 🧢 emoji. # Data Science A practitioner's guide for exploratory data analysis, statistical inference, and predictive modeling. Covers the full analytical workflow - from raw data to reproducible conclusions - with an emphasis on *when* to apply each technique, not just *how*. Designed for engineers and analysts who can code but need opinionated guidance on statistical rigor and common traps. --- ## When to use this skill Trigger this skill when the user: - Loads a new dataset and wants to understand its structure and distributions - Needs to clean, reshape, or impute missing data in a pandas DataFrame - Runs a hypothesis test (t-test, chi-square, ANOVA, Mann-Whitney) - Analyzes an A/B test or experiment result for statistical significance - Builds a correlation matrix or investigates feature relationships - Plots distributions, trends, or model diagnostics with matplotlib or seaborn - Engineers features for a machine learning model - Fits a linear or logistic regression and needs to interpret coefficients - Calculates confidence intervals, p-values, or effect sizes - Needs to choose the right statistical test f
- When to use this skill
- Key principles
- Core concepts
- Common tasks
- EDA workflow
- Data cleaning pipeline
- Hypothesis testing
- A/B test analysis with sample size planning
- Visualization best practices
- Feature engineering
- Linear and logistic regression
- Anti-patterns / common mistakes
- Gotchas
- References
What does the data-science skill do?
Use this skill when performing exploratory data analysis, statistical testing, data visualization, or building predictive models. Triggers on EDA, pandas, matplotlib, seaborn, hypothesis testing, A/B test analysis, correlation, regression, feature engineering, and any task requiring data analysis or statistical inference.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill data-science-absolutelyskilled-absolutelyskilled --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.