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carlheath
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Data Analysis Specialist

Analyze datasets with pandas, matplotlib, seaborn, and scipy for statistical insights.

9|5|Updated Nov 9, 2025
Search Tags:#insights#dashboard#data analysis#sql#visualization#statistics
One-click install
npx skills add https://github.com/carlheath/ogmios --skill data-analysis-specialist
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Data Analysis Specialist
Source: https://github.com/carlheath/ogmios/tree/main/.claude/skills/data-analysis
Command: npx skills add https://github.com/carlheath/ogmios --skill data-analysis-specialist

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill transforms raw data into actionable insights, helping you understand trends, make informed decisions, and communicate findings effectively through clear visualizations and statistical analysis.

Core Features & Use Cases

  • Exploratory Data Analysis (EDA): Understand dataset characteristics, identify patterns, and detect anomalies.
  • Statistical Analysis: Apply appropriate statistical tests and models to draw meaningful conclusions.
  • Data Visualization: Create compelling charts and graphs to illustrate insights.
  • SQL Querying: Extract and manipulate data from databases.
  • Use Case: Analyze customer purchase history to identify top-selling products, regional trends, and seasonal patterns, then present these findings in a dashboard.

Quick Start

Analyze the attached sales data CSV file and provide a summary of key trends.

Frequently Asked Questions about Data Analysis Specialist

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

FAQPage Schema
How do I perform exploratory data analysis on a CSV file?▼

Exploratory data analysis identifies dataset characteristics, patterns, and anomalies. You can analyze an attached CSV file to automatically generate a summary of key trends and visualize underlying data structures.

What statistical tests should I use for hypothesis testing?▼

Hypothesis testing applies statistical models to draw meaningful conclusions from data. Using scipy, you can execute appropriate statistical tests to validate hypotheses and extract actionable insights from your datasets.

How do I create a dashboard to visualize data trends?▼

Data visualization dashboards illustrate insights through charts and graphs. Using matplotlib and seaborn, you can generate compelling visualizations that communicate identified trends and patterns from your analysis effectively.

Can I extract and manipulate data using SQL queries?▼

SQL querying extracts and manipulates data directly from databases. You can write SQL queries to pull relevant records before applying statistical modeling and generating visualizations for deeper analysis.

Does this tool support Python libraries for statistical modeling?▼

Statistical modeling supports Python libraries including pandas, matplotlib, seaborn, and scipy. These frameworks enable comprehensive data analysis, from exploratory data processing to advanced hypothesis testing and visualization.

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