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README.md
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license: apache-2.0
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license: apache-2.0
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# 🧠 data_jobs Dataset
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A dataset of real-world data analytics job postings from 2023, collected and processed by Luke Barousse.
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## 📘 Data Dictionary
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| Column Name | Description | Type | Source |
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|-------------------------|-----------------------------------------------------------------------------|--------------|------------------|
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| `job_title_short` | Cleaned/standardized job title using BERT model (10-class classification) | Calculated | From `job_title` |
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| `job_title` | Full original job title as scraped | Raw | Scraped |
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| `job_location` | Location string shown in job posting | Raw | Scraped |
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| `job_via` | Platform the job was posted on (e.g., LinkedIn, Jobijoba) | Raw | Scraped |
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| `job_schedule_type` | Type of schedule (Full-time, Part-time, Contractor, etc.) | Raw | Scraped |
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| `job_work_from_home` | Whether the job is remote (`true`/`false`) | Boolean | Parsed |
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| `search_location` | Location used by the bot to generate search queries | Generated | Bot logic |
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| `job_posted_date` | Date and time when job was posted | Raw | Scraped |
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| `job_no_degree_mention` | Whether the posting explicitly mentions no degree is required | Boolean | Parsed |
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| `job_health_insurance` | Whether the job mentions health insurance | Boolean | Parsed |
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| `job_country` | Country extracted from job location | Calculated | Parsed |
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| `salary_rate` | Indicates if salary is annual or hourly | Raw | Scraped |
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| `salary_year_avg` | Average yearly salary (calculated from salary ranges when available) | Calculated | Derived |
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| `salary_hour_avg` | Average hourly salary (same logic as yearly) | Calculated | Derived |
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| `company_name` | Company name listed in job posting | Raw | Scraped |
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| `job_skills` | List of relevant skills extracted from job posting using PySpark | Parsed List | NLP Extracted |
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| `job_type_skills` | Dictionary mapping skill types (e.g., 'cloud', 'libraries') to skill sets | Parsed Dict | NLP Extracted |
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