Job Applications Tracker

A Python-based system for tracking and managing job applications, with tools for filtering companies, scraping job postings, and processing job data.

Overview

This project provides a comprehensive solution for job seekers to:

• Filter and identify target companies from large datasets
• Automatically scrape job postings from multiple job sites
• Process and organize job application data
• Track applications and manage the job search process

Features

• Company Filtering: Filter companies by industry, size, and location
• Job Scraping: Automated scraping from Indeed, LinkedIn, ZipRecruiter, Glassdoor, and more
• Data Processing: Convert job postings from markdown to structured CSV format
• Remote Job Focus: Specialized filtering for remote and hybrid positions
• Salary Filtering: Focus on high-paying opportunities

Project Structure

jobApplications/
├── filter_companies.py      # Filter companies from CSV data
├── internship_scraper.py    # Scrape job postings from job sites
├── job_postings.py         # Process markdown job data to CSV
├── requirements.txt        # Python dependencies
└── README.md              # This file

Scripts

filter_companies.py

Filters a CSV file of companies to identify target companies based on:

• Industry (tech and financial sectors)
• Company size (250+ employees)
• Location (US-based companies)

Usage:

python filter_companies.py

Input: companies_sorted.csv Output: financial_tech_companies_us.csv

internship_scraper.py

Scrapes job postings from multiple job sites for companies in your target list.

Features:

• Searches across Indeed, LinkedIn, ZipRecruiter, Glassdoor, and Google
• Filters for remote/hybrid positions
• Focuses on internships with competitive salaries
• Avoids duplicate postings
• Rate limiting to prevent blocking

Usage:

python internship_scraper.py

Input: financial_tech_companies_us.csv Output: internships.csv

job_postings.py

Processes markdown job postings and converts them to structured CSV format.

Features:

• Extracts job data from markdown tables
• Cleans and standardizes job information
• Filters by location (e.g., remote positions)
• Handles multiple application links

Usage:

python job_postings.py

Input: README.md (with job table) Output: jobs.csv

Installation

1. Clone the repository:

git clone <repository-url>
cd jobApplications

2. Install dependencies:

pip install -r requirements.txt

Dependencies

• pandas: Data manipulation and analysis
• matplotlib: Data visualization
• seaborn: Statistical data visualization
• python-jobspy: Job scraping library
• beautifulsoup4: HTML parsing
• pathlib: File path handling

Usage Workflow

1. Prepare Company Data: Start with a CSV file containing company information
2. Filter Companies: Run filter_companies.py to identify target companies
3. Scrape Jobs: Use internship_scraper.py to find relevant job postings
4. Process Data: Use job_postings.py to clean and organize job data
5. Track Applications: Use the generated CSV files to track your applications

Configuration

Company Filtering

Modify the industry lists in filter_companies.py:

tech_industries = [
    'information technology and services',
    'computer software',
    # Add more industries as needed
]

Job Scraping

Adjust search parameters in internship_scraper.py:

• results_wanted: Number of results per search
• hours_old: How recent jobs should be
• distance: Search radius for location-based jobs

Job Processing

Configure location filtering in job_postings.py:

location_filter = "Remote in USA"  # Set to None to disable filtering

Output Files

• financial_tech_companies_us.csv: Filtered list of target companies
• internships.csv: Scraped job postings with details
• jobs.csv: Processed job data from markdown sources

Tips for Success

1. Start Small: Begin with a focused list of target companies
2. Regular Updates: Run the scraper regularly to catch new postings
3. Customize Filters: Adjust salary and location filters based on your preferences
4. Track Applications: Use the CSV outputs to maintain your application records
5. Rate Limiting: Be respectful of job site rate limits to avoid blocking

Troubleshooting

Common Issues

Scraping Errors: If you encounter rate limiting or blocking:

• Increase delays between requests
• Reduce batch sizes
• Use different search terms

Data Quality: For better results:

• Verify company names in your input CSV
• Check that job sites are accessible
• Review and clean scraped data

Performance: For large datasets:

• Process companies in smaller batches
• Use appropriate delays between requests
• Monitor memory usage with large CSV files

Contributing

This is a personal project for job application tracking. Feel free to fork and modify for your own use.

License

This project is for personal use. Please respect the terms of service of job sites when scraping data.