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harvard-artifacts-collection-analytics-etl

Extract, transform, and load Harvard Art Museums API data into a SQL database.

5|1|Updated May 16, 2026
Search Tags:#SQL#ETL#data analysis#Streamlit#Plotly
One-click install
npx skills add https://github.com/Aradotso/data-skills --skill harvard-artifacts-collection-analytics-etl
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: harvard-artifacts-collection-analytics-etl
Source: https://github.com/Aradotso/data-skills/tree/main/skills/harvard-artifacts-collection-analytics-etl
Command: npx skills add https://github.com/Aradotso/data-skills --skill harvard-artifacts-collection-analytics-etl

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, requests, mysql-connector-python, plotly, python-dotenv, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill addresses the challenge of analyzing complex datasets from the Harvard Art Museums API, providing a streamlined ETL pipeline and interactive analytics.

Core Features & Use Cases

  • ETL Pipeline: Extracts, transforms, and loads data from the Harvard Art Museums API into a SQL database.
  • Data Visualization: Offers interactive dashboards for visualizing art museum data with Plotly.
  • Use Case: For data analysts or researchers who need to perform in-depth analysis on the vast collection of artifacts at the Harvard Art Museums.

Quick Start

Run the ETL pipeline to load data into the database and then visualize the number of artifacts by culture using the Streamlit dashboard.

Frequently Asked Questions about harvard-artifacts-collection-analytics-etl

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

FAQPage Schema
How do I extract Harvard Art Museums API data into a SQL database?▼

An automated ETL pipeline extracts Harvard Art Museums API data using requests and loads it into a SQL database via mysql-connector-python, yielding structured data ready for querying.

What is the best way to visualize art museum data with Plotly in Python?▼

Visualizing art museum data with Plotly is handled through a Streamlit dashboard that renders interactive charts, allowing you to explore artifact distributions by culture directly in your browser.

Do I need pandas to transform API data before loading it into SQL?▼

Yes, you need pandas to transform API data before loading it into SQL, as the ETL pipeline relies on it to clean and structure the raw records from the Harvard Art Museums API.

Can I use Streamlit to build an interactive dashboard for artifact analytics?▼

You can use Streamlit to build an interactive dashboard for artifact analytics, providing a web interface that displays Plotly visualizations to explore the Harvard Art Museums collection.

Does this ETL pipeline support visualizing artifacts by culture?▼

The ETL pipeline supports visualizing artifacts by culture by loading the transformed data into a SQL database and rendering it through a Streamlit dashboard equipped with interactive Plotly charts.

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