End-to-End Analysis of LinkedIn Job Postings Using Python and Power BI

In today's competitive job market, understanding hiring trends and job posting patterns is crucial for job seekers, recruiters, and analysts alike. Traditional job portals often lack insights into evolving skill demands and recruitment behavior [2]. This paper introduces a comprehensive analysis system for LinkedIn Job Postings and Hiring Trends, leveraging data visualization and business intelligence techniques [4]. Using a cleaned dataset of scraped LinkedIn job postings, the system identifies top in demand roles, frequently required skills, salary patterns, experience levels, and regional hiring dynamics [1]. The solution utilizes Power BI for interactive dashboards, enriched with DAX measures to uncover hidden patterns and relationships ([3]). Additionally, Python and Excel were used for data preprocessing, ensuring data quality and consistency. The resulting dashboards support multi-angle exploration—industry-wise hiring, job level analysis, function vs. experience mapping, and skill gaps— providing actionable insights for career planning and workforce development. This data-driven approach empowers stakeholders with a deeper understanding of the professional landscape and future hiring trajectories.

@article{ijter2025212507210410,
  author = {Jashmita Boyina and Prof. Ch Satyananda Reddy},
  title = {End-to-End Analysis of LinkedIn Job Postings Using Python and Power BI},
  journal = {International Journal of Technology &  Emerging Research },
  year = {2025},
  volume = {1},
  number = {3},
  pages = {65-75},
  issn = {3068-109X},
  url = {https://www.ijter.org/article/212507210410/end-to-end-analysis-of-linkedin-job-postings-using-python-and-power-bi},
  abstract = {In today's competitive job market, understanding hiring trends and job posting patterns is crucial for job seekers, recruiters, and analysts alike. Traditional job portals often lack insights into evolving skill demands and recruitment behavior [2]. This paper introduces a comprehensive analysis system for LinkedIn Job Postings and Hiring Trends, leveraging data visualization and business intelligence techniques  [4]. Using a cleaned dataset of scraped LinkedIn job postings, the system identifies top in demand roles, frequently required skills, salary patterns, experience levels, and regional hiring dynamics [1]. The solution utilizes Power BI for interactive dashboards, enriched with DAX measures to uncover hidden patterns and relationships ([3]). Additionally, Python and Excel were used for data preprocessing, ensuring data quality and consistency. The resulting dashboards support multi-angle exploration—industry-wise hiring, job level analysis, function vs. experience mapping, and skill gaps— providing actionable insights for career planning and workforce development. This data-driven approach empowers stakeholders with a deeper understanding of the professional landscape and future hiring trajectories.},
  keywords = {LinkedIn Data, Power BI Dashboards, Data Visualization, Python, Excel, DAX Measures},
  month = {Jul},
}

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