Dataset Overview:
The collection spans a variety of roles, from Assistant Civil Engineers to Construction Project Managers, detailed across several entries. Each record is a window into the diverse opportunities available, reflecting the sector's breadth and depth.
Data Science Applications:
This dataset is primed for analytical explorations, from trend analysis and market demand assessments to skill gap identification. Its structured format is ideal for machine learning applications, such as predictive analytics for career path advancements or salary predictions, and NLP tasks like job description analysis or automated matching of candidates to job listings.
Column Descriptors:
- company_name: The hiring entity's name.
- job_title: The specific role or position offered.
- job_description: A detailed account of the role, responsibilities, and qualifications.
- job_posted_date: Date the job listing was published.
- job_salary/currency, job_salary/currency_symbol, job_salary/estimated: Salary information including currency, symbol, and whether the salary is an estimate.
- job_salary/pay_period: The salary distribution frequency (e.g., hourly, annually).
- job_industry: The industry segment the job belongs to.
- company_short_name: An abbreviated form of the company's name.
- company_sizes_str: The size category of the company based on the number of employees.
- company_foundation_date: The year the company was established.
- company_revenue: The company's revenue range.
Ethically Mined Data:
The dataset is constructed with ethical considerations at the forefront, ensuring the information is publicly available, non-invasive, and respectful of privacy norms. It adheres to data protection guidelines, offering insights without compromising individual or corporate confidentiality.
Acknowledgment:
A nod to Glassdoor for its role in aggregating and making accessible such detailed job market data. This dataset leverages the platform's rich repository of job listings, providing a snapshot that's invaluable for analysis, insight generation, and decision-making in the job market.