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data-analyst

Analyze datasets using SQL, pandas, and statistical methods.

4|1|Updated Nov 1, 2025
Search Tags:#data analysis#sql#pandas#hypothesis testing#statistics#data exploration#data cleaning
One-click install
npx skills add https://github.com/xtrm-dev/specialists --skill data-analyst-xtrm-dev
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-analyst
Source: https://github.com/xtrm-dev/specialists/tree/main/.xtrm/skills/optional/data-engineering/data-analyst
Command: npx skills add https://github.com/xtrm-dev/specialists --skill data-analyst-xtrm-dev

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you explore datasets, answer questions, and turn raw data into clear findings without manually stitching together SQL, pandas, and statistics.

Core Features & Use Cases

  • SQL Analysis: Write queries to extract, join, aggregate, and reshape data from databases.
  • pandas Workflows: Clean, transform, filter, and summarize datasets in Python.
  • Statistical Insight: Compute descriptive statistics, test hypotheses, and look for meaningful patterns.
  • Use Case: If you need to investigate sales performance, you can pull the data with SQL, clean and reshape it in pandas, and summarize the trends with statistical analysis.

Quick Start

Use the data analyst skill to analyze this dataset, write the needed SQL or pandas code, and explain the key insights in plain language.

Frequently Asked Questions about data-analyst

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

FAQPage Schema
How do I analyze a dataset using SQL and pandas?▼

To analyze a dataset using SQL and pandas, you extract and join data with SQL queries, then clean, transform, and summarize it in pandas to uncover patterns and actionable insights.

What is the best way to clean data and test hypotheses in Python?▼

The best way to clean data and test hypotheses in Python is applying reproducible pandas operations for data cleaning, followed by descriptive and inferential statistics for hypothesis testing.

Can I use this for exploratory data analysis and reporting workflows?▼

Yes, you can use this for exploratory data analysis and reporting workflows. It processes datasets through data exploration and statistical investigation to deliver concise interpretation of results.

Do I need a database to run SQL queries for data exploration?▼

You need a structured data source to run SQL queries for data exploration, ensuring a clear query structure to extract, join, and aggregate data for your analysis.

How does statistical investigation work with pandas transformations?▼

Statistical investigation with pandas transformations works by filtering and reshaping raw datasets in pandas, then computing descriptive statistics to look for meaningful patterns and relationships.

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