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data-analyst

Analyze datasets with SQL, pandas, and statistical methods.

Updated Mar 8, 2026
Search Tags:#dashboards#sql#pandas#statistics#data-analysis#data-cleaning#data-exploration
One-click install
npx skills add https://github.com/Phife726/ds_agent --skill data-analyst-phife726
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-analyst
Source: https://github.com/Phife726/ds_agent/tree/main/data-analyst
Command: npx skills add https://github.com/Phife726/ds_agent --skill data-analyst-phife726

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

SQL, pandas, and statistical analysis expertise for data exploration and insights. This skill helps translate raw data into actionable findings, enabling informed decisions.

Core Features & Use Cases

  • Data exploration and cleaning using SQL and pandas.
  • Statistical analysis including descriptive statistics and hypothesis testing.
  • Data transformations and aggregations to prepare data for reporting, dashboards, and modeling.

Quick Start

Ask the AI to analyze a dataset by writing SQL queries and a pandas workflow to reveal key insights.

Frequently Asked Questions about data-analyst

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

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

To clean and explore a dataset, you can use SQL queries for data extraction and pandas for data transformation, handling missing values, and aggregating records to prepare for reporting and dashboards.

What is the best way to run statistical analysis and hypothesis testing on raw data?▼

Statistical analysis on raw data involves applying descriptive statistics and hypothesis testing using Python and pandas, translating your datasets into actionable findings and clear insights for informed decisions.

Can I use this approach for ad hoc data analysis and dashboard reporting?▼

Yes, you can apply SQL and pandas workflows to ad hoc data analysis, performing data transformations and aggregations to prepare datasets specifically for dashboard reporting and modeling.

Do I need Python and SQL proficiency to perform data transformations and aggregations?▼

Yes, SQL proficiency and Python knowledge with pandas are required to perform data queries, transformations, aggregations, and statistical hypothesis testing across diverse datasets.

How do I extract actionable insights from diverse datasets for decision making?▼

Extracting actionable insights from diverse datasets requires applying statistical methods, SQL queries, and pandas workflows to explore, clean, and transform raw data into clear findings.

What's the difference between data cleaning and data exploration in a pandas workflow?▼

Data cleaning in a pandas workflow fixes missing values and formats, while data exploration queries the dataset to reveal key insights, both essential steps before reporting and statistical analysis.

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