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

Abstract

In today’s competitive job market, analysing job trends is essential for both job seekers and organisations. This project focuses on extracting meaningful insights from LinkedIn job data. The system collects job-posting data including job titles, locations, required skills, company details, and salary information. Python is used for data collection, cleaning, preprocessing, and analysis, with libraries such as Pandas, NumPy, and Matplotlib applied to identify patterns and trends in job postings. The processed data is then visualised using Power BI to create interactive dashboards that provide clear insights into job demand, skill requirements, location-based opportunities, and industry trends. The project helps users understand current job-market trends, identify in-demand skills, and make informed career decisions, and it assists organisations in analysing hiring patterns and improving recruitment strategies. Unlike platforms that present raw listings without structured analytics, the proposed system provides a complete end-to-end pipeline—from data extraction through preprocessing and analysis to interactive visualisation—within a unified workflow. The system was validated through eight functional test cases covering dataset loading, data cleaning, skill extraction, dashboard loading, filtering, skill search, data download, and error handling, all of which passed. Overall, the system provides a user-friendly and efficient solution for jobdata analysis and smarter decision-making in the evolving job market.