For organizations looking to gain valuable insights into their operations and customer base, the use of data has become increasingly important. Organizations have a unique opportunity to use the vast amount of data generated by various sources, such as customer interactions, sales transactions, and social media, to make informed decisions. Data analytics, which involves the use of statistical techniques and machine learning algorithms to uncover patterns, trends, and relationships within data, is one-way organizations are accomplishing this. Data analytics can provide organizations with insights into their operations and customers, resulting in increased efficiency and satisfaction. Organizations, for example, can gain insights into customer preferences and tailor their marketing efforts accordingly by analyzing customer interactions and purchase history. Furthermore, data analytics can assist organizations in identifying inefficiencies in their operations, such as supply chain bottlenecks, and optimizing processes to increase productivity and reduce costs. Overall, data analytics implementation can provide organizations with a competitive advantage by assisting them in making informed decisions based on insights derived from their data. This research aims to look into the predictive tools used in the labor market and assess the impact of web scraping, NLP, and LDA in analyzing job postings, comparing them to a CV with specific skills, and then providing a similarity rate of the keyword matching. We will look specifically at how these techniques can be used to identify critical job requirements and forecast future job opportunities for students and recent graduates. We will also evaluate the limitations of these techniques and make recommendations for future research.