# Analyze Data with Statistical Rigor

Complex data questions can produce unreliable conclusions when methods, assumptions, and business goals are unclear. This skill structures analysis, modeling, validation, and communication into a rigorous workflow.

## Install

```bash
npx skillstore add sickn33/data-scientist
```

## Metadata

- Status: approved
- Slug: sickn33-data-scientist
- Skillstore revision: r2
- Version status: missing
- Tree hash: f97870d903f3c9d812691f36f6f9001d30a40f8df60ad190fbe3775eaf83ee2d
- Author: sickn33
- GitHub username: sickn33
- License: MIT
- Repository: https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/data-scientist
- Ref: f9e2c34b4f19c7f3e6b0a1e93227b5f77cc12526
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: allowed
- Manual install advisory: allowed
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Quality score: 78
- Quality tier: bronze
- Public page: https://skillstore.pages.dev/skills/sickn33-data-scientist
- Manifest: https://skillstore.pages.dev/api/skills/sickn33-data-scientist/manifest

## Capabilities

- Designs statistical analyses, hypothesis tests, experiments, power calculations, and causal inference plans.
- Guides supervised, unsupervised, time series, deep learning, and ensemble modeling workflows.
- Structures exploratory analysis around data quality, distributions, relationships, anomalies, and business context.
- Recommends evaluation metrics, validation methods, interpretability techniques, visualizations, and stakeholder reporting.
- Plans model deployment, monitoring, governance, reproducibility, and analytical data pipelines.

## Use Cases

- Evaluate a Product Experiment: Define metrics, estimate sample size, test outcomes, and explain practical significance for a product decision.
- Develop a Predictive Model: Plan features, validation, model comparison, interpretability, deployment, and monitoring for a prediction task.
- Forecast Operational Demand: Assess seasonality, choose forecasting methods, quantify uncertainty, and connect predictions to inventory decisions.

## Prompt Templates

### Explore a Dataset

```
Review [dataset description] for [business goal]. Identify quality issues, useful summaries, visualizations, assumptions, and next analytical steps.
```

### Design an Experiment

```
Design an experiment for [intervention] and [outcome]. Specify hypotheses, metrics, randomization, sample size inputs, validity risks, and analysis steps.
```

### Plan a Predictive Model

```
Create a modeling plan for predicting [target] from [available data]. Cover leakage, features, baselines, validation, metrics, interpretation, and monitoring.
```

### Assess Causal Impact

```
Evaluate the causal effect of [exposure] on [outcome] using [observational data]. Compare identification strategies, assumptions, diagnostics, sensitivity tests, and limitations.
```

## Limitations

- The skill cannot inspect unavailable datasets, systems, dashboards, or model artifacts.
- Recommendations still require domain expertise, privacy review, and environment-specific validation.
- The skill does not guarantee causal conclusions, model performance, or production reliability.
- Tool guidance may require adjustment for current library versions and infrastructure constraints.

## Best Practices

- Define the decision, target population, outcome, and success criteria before choosing methods.
- Validate assumptions, leakage controls, uncertainty estimates, robustness, and subgroup behavior.
- Document data provenance, transformations, parameters, results, limitations, and monitoring requirements.

## Anti Patterns

- Do not select a complex model before establishing a relevant baseline.
- Do not interpret correlation, feature importance, or predictive accuracy as causal evidence.
- Do not report a single metric without uncertainty, validation design, and business context.

## Security Audit

- Audited at: 2026-07-23T22:17:44.968\+00:00
- Summary: The skill contains only instructional Markdown and no executable code, network actions, credential access, or destructive operations. No static or semantic security findings were identified, including no prompt injection language.

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