📅 2024-01-19 — Session: Web Scraping LinkedIn and Economic Analysis
🕒 15:50–17:25
🏷️ Labels: Web Scraping, Linkedin, Economic Analysis, Data Science, Career Development
📂 Project: Business
⭐ Priority: MEDIUM
Session Goal
The session aimed to explore web scraping techniques for LinkedIn job postings and analyze economic data.
Key Activities
- Developed a structured approach to scrape LinkedIn job postings using Python, focusing on libraries like BeautifulSoup and requests, and handling login authentication while considering LinkedIn’s terms of service.
- Analyzed a JavaScript snippet used for redirection on LinkedIn, focusing on tracking code parsing and HTTPS redirection.
- Conducted a strengths and weaknesses analysis for a data science profile, highlighting educational background and technical skills.
- Discussed key aspects for data science candidates to stand out, including technical skills and communication.
- Expanded a detailed price list across economic categories, enabling granular trend analysis.
- Provided a detailed breakdown of economic costs and salaries, with specific examples.
Achievements
- Established a comprehensive method for LinkedIn job scraping, including compliance considerations.
- Gained insights into LinkedIn’s redirection scripts and their implications for web scraping.
- Identified strengths and areas for improvement in a data science career profile.
- Enhanced understanding of economic trends through detailed category analysis.
Pending Tasks
- Implement the LinkedIn web scraping strategy in a live environment, ensuring compliance with terms of service.
- Further explore economic data analysis techniques for more detailed insights.