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harvard-artifacts-etl-streamlit-analytics

Extract Harvard Art Museums API data into SQL tables and Streamlit visualizations.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires streamlit, 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 automates the ETL (Extract, Transform, Load) process and analytics for Harvard Art Museums data, enabling developers to build end-to-end data engineering and analytics applications.

Core Features & Use Cases

  • ETL Pipeline: Extract data from the Harvard Art Museums API, transform it into structured SQL tables, and load it into a database.
  • SQL Analytics: Pre-built analytical queries for insights on culture, century, media availability, and color patterns.
  • Interactive Dashboard: Build interactive Streamlit visualizations for query results.
  • Use Case: Developers can use this Skill to create a Streamlit dashboard that visualizes the number of artifacts by culture, century, and department.

Quick Start

Install the skill and run the Streamlit app to visualize Harvard museum collection data.

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

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

FAQPage Schema
How do I build an ETL pipeline for Harvard Art Museums API data?▼

You can build an ETL pipeline by extracting data from the Harvard Art Museums API, transforming it into structured SQL tables, and loading it into a MySQL database for analytics workflows.

How do I visualize SQL query results from museum collections in Streamlit?▼

You can visualize SQL query results in Streamlit by running pre-built analytical queries on culture, century, and color patterns, then rendering the output as interactive Plotly data visualizations.

What is the best way to analyze cultural institution data using Python and SQL?▼

Analyzing cultural institution data involves extracting API records into pandas, transforming them into structured SQL tables, and querying insights on media availability and color patterns for dashboard visualization.

Do I need MySQL to use Streamlit for data analytics on museum artifacts?▼

Yes, you need MySQL to load and store the transformed Harvard Art Museums data, alongside Python, Streamlit, pandas, and plotly to process and visualize the analytical query results.

Can I extract and transform Harvard Art Museums data without writing custom SQL queries?▼

Yes, this approach provides pre-built analytical SQL queries for insights on culture, century, media availability, and color patterns, automating the transformation into structured SQL tables.

What are the limitations of using Streamlit and Plotly for cultural data analytics?▼

Streamlit and Plotly visualizations are limited to the data extracted from the Harvard Art Museums API, requiring a MySQL database to manage the structured SQL tables before rendering interactive dashboards.

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