skills.rest
gagan114662
gagan114662COMMUNITY
Explore all skills by this author →
GitHub

data-analyst

Analyze datasets with pandas, numpy, matplotlib, and seaborn for statistics and visualization.

Updated Feb 26, 2026
Search Tags:#data analysis#visualization#pandas#statistics#data cleaning#eda
One-click install
npx skills add https://github.com/gagan114662/exp --skill data-analyst-gagan114662
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-analyst
Source: https://github.com/gagan114662/exp/tree/main/crates/openfang-skills/bundled/data-analyst
Command: npx skills add https://github.com/gagan114662/exp --skill data-analyst-gagan114662

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps users analyze data, identify trends, and gain actionable insights by performing statistical analysis, data cleaning, and creating visualizations.

Core Features & Use Cases

  • Exploratory Data Analysis (EDA): Inspect data quality, distributions, and relationships.
  • Data Cleaning: Handle missing values, standardize formats, and remove duplicates.
  • Statistical Analysis: Compute descriptive statistics and perform hypothesis testing.
  • Visualization: Create informative charts (bar, line, scatter, histogram) to communicate findings.
  • Use Case: Analyze customer feedback data to identify common themes and sentiment trends, then visualize the most frequent issues.

Quick Start

Analyze the provided dataset 'customer_feedback.csv' to identify the top 5 most common issues.

Frequently Asked Questions about data-analyst

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

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

Data cleaning handles missing values, standardizes formats, and removes duplicates using pandas. This process ensures your dataset is properly formatted and validated for accurate statistical analysis downstream.

Can I use pandas and matplotlib for statistical analysis and visualization?▼

Yes, you can use pandas and matplotlib for statistical analysis and visualization. This Skill computes descriptive statistics, performs hypothesis testing, and creates informative charts like bar, line, scatter, and histogram visualizations.

What is the best way to clean data and handle missing values before analysis?▼

Exploratory data analysis on a CSV dataset involves inspecting data quality, distributions, and relationships. This Skill uses pandas and numpy to validate data quality and perform insightful EDA to identify initial trends.

Does this data analysis approach work for identifying trends in customer feedback?▼

Yes, this approach works for identifying trends in customer feedback by analyzing the data to find common themes and sentiment trends. It then visualizes the most frequent issues to communicate actionable findings effectively.

How do I create reproducible data cleaning and statistical inference workflows?▼

Reproducible data cleaning and statistical inference workflows are created by applying consistent data quality validation and hypothesis testing using Python libraries. This Skill satisfies requirements for standardizing formats and computing descriptive statistics reliably.

Related Skills

Explore More Data & Analytics Skills→
omicverseomicverse
32

data-transform

Clean, reshape, normalize and engineer features from CSV/TSV/Parquet inputs using pandas and numpy locally.

Official
Advanced
yangqiongyangqiong

Report Generator Skill

Generate HTML data reports from pandas DataFrames with summaries and analyses.

Community
Intermediate
rancapolyrancapoly

employee-voice-synthesizer

Analyze employee feedback channels with NLP theme detection and classification.

Community
Advanced
Mohamed-ElkahefMohamed-Elkahef

data-analyst

Clean missing values in CSV datasets and generate Plotly Dash dashboards.

Community
Intermediate