Does it provide data interpretations or just code?

It is designed to provide both. Along with the Python code, it generates observations and actionable business intelligence based on the results.

Can it handle missing or messy data?

Yes, the skill includes specific instructions for detecting missing values and suggesting cleaning strategies such as imputation or removal.

Is this skill suitable for predictive modeling?

While it focuses on exploratory analysis (EDA) and visualization, it provides the essential data preparation foundation required for machine learning.

What visualization libraries does the skill use?

It primarily utilizes Matplotlib and Seaborn for statistical charts, and Plotly for creating interactive visualizations.

Which data formats does this skill support?

The Data Analyst skill is designed to work with common structured data formats including CSV, Excel, JSON, and SQL database outputs.

Data Analyst

Name: Data Analyst
Author: k1lgor

byk1lgor

0•

데이터 과학 및 ML

Transforms raw data into actionable insights through automated exploratory analysis and professional Python-based visualizations.

The Data Analyst skill empowers AI agents to perform end-to-end data processing, from initial cleaning to the generation of complex visual reports. By leveraging industry-standard libraries like Pandas, Seaborn, and Plotly, this skill automates the identification of trends, correlations, and outliers within datasets. It moves beyond simple code generation by providing narrative explanations of findings, helping users understand the 'why' behind the numbers. Whether you are analyzing sales performance, user behavior, or financial metrics, this skill provides a structured workflow for professional-grade business intelligence.

주요 기능

01Comprehensive Exploratory Data Analysis (EDA) with descriptive statistics

02Smart visualization selection for trends, comparisons, and distributions

03Automated data cleaning including missing value and duplicate handling

040 GitHub stars

05Interactive and static chart generation using Plotly and Seaborn

06Insight extraction with narrative summaries and actionable recommendations

사용 사례

01Analyzing sales datasets to identify seasonal trends and top revenue drivers

02Comparing demographic distributions between churned and retained users

03Generating correlation heatmaps to find relationships between business variables

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add k1lgor/virtual-company 16-data-analyst

For use in Claude.ai and ChatGPT

Download Skill