In this project, you will step into the shoes of an entry-level data analyst at medium-sized recruitment agency, helping to improve its sourcing of job vacancies.
The team at the recruitment agency is trying to improve its sourcing of job vacancies. To do this the agency relies on multiple job posting sites to identify potential job openings for its clients. However, manually searching through each site is time-consuming and often leads to missed opportunities.
They want you to analyze the data using web scraping tools that can automatically extract job posting data from multiple job posting sites. The team will use your analysis to provide a more efficient way to provide job vacancies to better serve its clients. This feature will help the recruitment agency by getting relevant openings to their clients more quickly, giving their clients a competitive advantage over other applicants.
Increase the efficiency of job vacancy sourcing
Improve the quality of job vacancy sourcing
Gain a competitive advantage
Your task will be to conduct a web scraping data analysis to automatically extract job posting data from a job posting site. To do this, you will set up the environment, identify the job posting site, scrape the data, process, analyze, and visualize the data.
In this project, we'll use data from a job posting site; however, to keep this project unique and open-ended, please feel free to choose whichever major job posting website you'd prefer - please note that some sites have an anti-scraping filter which can affect your results.
After you perform your analysis, you will share your findings.