Analysis of the Turkish job market and ict industry labor demands using web scraping

This research provides a comprehensive analysis of Turkish job market trends using web scraping. Utilizing Octoparse8, we extracted a wealth of job postings data from online job platforms like LinkedIn and Kariyer.net. The data was then prepared and cleaned using Python's Pandas library, involving processes such as importing, formatting, sampling, handling missing values, removing duplicates, data type conversions, and outlier treatment. Our analysis tested several hypotheses and explored various relationships within the job market, including those between company size, job advertisement frequency, and in-demand programming skills. We found a positive correlation between larger corporations and the number of job ads. Furthermore, our study identified the most sought-after programming skills in the Turkish market, such as Javascript, SQL, HTML, C#, and Python, and highlighted high-demand job roles and sectors with significant job growth rates. v We employed various data visualization techniques, including bar plots, histograms, scatter plots, heatmaps, box plots, and pie charts, to present the data in a graphical format, facilitating a better understanding of patterns, trends, and insights in the job market. The findings from this study not only contribute to the existing literature on the application of web scraping and data aggregation techniques in job market analysis, but also provide valuable insights for various stakeholders in the job market ecosystem, including job seekers, employers, educational institutions, and policymakers. The study demonstrates the power of Python and Jupyter Notebook in conducting such an analysis, highlighting the utility of libraries like Pandas, Matplotlib, and Seaborn. This research lays the groundwork for future predictive job market models.