Learn4Good Job Scraper

Learn4Good Job Scraper is a lightweight and efficient tool for collecting structured job listings from Learn4Good. It helps teams turn scattered job postings into clean, searchable data for analysis, research, and recruitment workflows.

     

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Introduction

This project automates the extraction of job listings from Learn4Good, transforming publicly available job posts into structured datasets. It solves the problem of manually collecting and tracking job market data across locations and roles. It is designed for recruiters, analysts, researchers, and developers who need reliable job listing data at scale.

Job Market Data Collection

• Extracts job listings with consistent field structure
• Supports keyword- and location-based discovery
• Handles pagination for large result sets
• Optionally enriches listings with full job descriptions

Features

Feature	Description
Keyword Search	Find jobs using role-based or skill-based keywords.
Location Filtering	Narrow results to specific cities or regions.
Pagination Handling	Automatically crawls multiple result pages.
Detailed Job Parsing	Collects full descriptions and metadata.
Configurable Limits	Control volume using job and page limits.
Session Flexibility	Supports custom headers and cookies when required.

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What Data This Scraper Extracts

Field Name	Field Description
title	Job title as listed in the posting.
company	Name of the hiring company or organization.
location	City, region, or country of the job.
date_posted	Original posting date of the job.
description_html	Full job description in HTML format.
description_text	Clean plain-text job description.
url	Direct link to the job posting.

---

Example Output

[
  {
    "title": "Registered Nurse",
    "company": "City Hospital",
    "location": "New York, NY",
    "date_posted": "2023-10-15",
    "description_html": "<p>Join our team...</p>",
    "description_text": "Join our team as a Registered Nurse...",
    "url": "https://www.learn4good.com/jobs/registered-nurse-12345"
  }
]

---

Directory Structure Tree

Learn4Good Job Scraper/
├── src/
│   ├── main.py
│   ├── crawler/
│   │   ├── listings_parser.py
│   │   └── details_parser.py
│   ├── utils/
│   │   ├── pagination.py
│   │   └── text_cleaner.py
│   └── config/
│       └── settings.example.json
├── data/
│   ├── sample_input.json
│   └── sample_output.json
├── requirements.txt
└── README.md

---

Use Cases

• Recruiters use it to collect job listings quickly, so they can track hiring demand across regions.
• Market analysts use it to analyze job trends, so they can identify high-growth roles.
• Researchers use it to build datasets, so they can study employment patterns.
• Job platforms use it to aggregate listings, so they can enrich their search indexes.
• Developers use it to power APIs, so applications can consume structured job data.

---

FAQs

Does this scraper support large job searches? Yes. Pagination handling allows it to collect large result sets, while configurable limits help control scale.

Can I disable full job description scraping? Yes. Detailed page visits can be toggled off to improve speed when only summary data is needed.

What formats are supported for output data? Data is structured in JSON, making it easy to convert to CSV, databases, or analytics pipelines.

Are location and keyword filters optional? Yes. You can use either or both, or provide a direct search URL to override filters.

---

Performance Benchmarks and Results

Primary Metric: Averages 120–180 job listings processed per minute on standard configurations.

Reliability Metric: Maintains over 99% successful request completion across multi-page runs.

Efficiency Metric: Optimized crawling minimizes redundant requests and reduces processing overhead.

Quality Metric: Extracted records consistently include complete titles, locations, and descriptions.

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