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data-analysis

Plan and execute exploratory data analysis with statistical testing and visualization planning.

Updated Aug 27, 2026
Search Tags:#insights#visualization#statistics#data-analysis#EDA#analyze#data-quality
One-click install
npx skills add https://github.com/pjordan/claude-toolkit --skill data-analysis-pjordan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-analysis
Source: https://github.com/pjordan/claude-toolkit/tree/main/skills/examples/data-analysis
Command: npx skills add https://github.com/pjordan/claude-toolkit --skill data-analysis-pjordan

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill guides a systematic approach to turning raw datasets into actionable insights by performing exploratory data analysis, statistical checks, data quality assessments, and thoughtful visualization planning.

Core Features & Use Cases

  • Structured analytics workflow: from data understanding to insights documentation.
  • Descriptive statistics & relationships: compute central tendency, variability, correlations, and group comparisons.
  • Data quality & readiness: identify missing values, outliers, duplicates, and inconsistencies.
  • Visualization planning: choose effective charts and visual storytelling for stakeholders.
  • Use Case: Analysts can quickly summarize a new dataset, compare segments, and prepare a data-driven report.

Quick Start

Provide Claude with a dataset description or sample data and ask for an end-to-end analysis, including data quality checks, descriptive statistics, relationships, visualization planning, and insights generation.

Frequently Asked Questions about data-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform exploratory data analysis on a new dataset?▼

Exploratory data analysis involves systematically understanding your dataset through data quality checks, descriptive statistics, and relationship assessments to summarize central tendency, variability, and correlations before generating insights.

What is the best way to assess data quality and readiness before reporting?▼

Data quality assessment identifies missing values, outliers, duplicates, and inconsistencies within your dataset to ensure readiness, applying structured checks that prepare clean inputs for downstream statistical testing and analytics pipelines.

Can I automate statistical testing and visualization planning for business analytics?▼

You can automate statistical testing and visualization planning by applying a structured analytics workflow that computes group comparisons and selects effective charts for visual storytelling to prepare data-driven reports for stakeholders.

How do I document insights from descriptive statistics and correlation assessments?▼

Document insights from descriptive statistics by following an end-to-end analysis workflow that evaluates relationships, computes central tendency and variability, and structures the findings into a comprehensive results documentation.

Does structured data analysis work for both research datasets and business reporting pipelines?▼

Structured data analysis works across business reporting, research, and analytics pipelines by applying consistent workflow steps including initial exploration, statistical checks, and data quality assessment to turn raw datasets into actionable insights.

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