Project Overview
Project Overview
This project developed an automated web scraping system to systematically collect data from Kursnet, the Federal Employment Agency's continuing education database. The scraped data formed the foundation for scientific qualitative evaluation of continuing education offerings in Germany.
Technical Highlights
- Intelligent Scraping: Adaptive scraping algorithms that handle dynamic content
- Data Quality Assurance: Built-in validation and error handling
- Scalable Architecture: Designed to handle thousands of course listings
- Research-Ready Output: Structured data export for qualitative analysis
Implementation Details
The scraper was built with:
- Scraping Engine: Node.js with Puppeteer for handling JavaScript-rendered content
- Data Processing: TypeScript for type-safe data transformation
- Storage: MongoDB for flexible schema and easy querying
- Batch Processing: Queue-based system for reliable large-scale scraping
Research Contribution
This tool enabled:
- Analysis of continuing education trends
- Identification of skill gaps in the job market
- Evaluation of regional differences in course offerings
- Published research in peer-reviewed journal
Publication
The findings from this project were published in a peer-reviewed article discussing the implications for adult education policy.
Tech Stack
Node.js
TypeScript
Puppeteer
MongoDB
Express
Queue Management
Data Validation
Security Features
Rate Limiting
Respectful scraping with built-in rate limiting and delays
Data Anonymization
Personal data automatically anonymized during collection
Secure Storage
Encrypted database storage for collected data