High-quality education is one of the efficient tools to ensure the sustainable development of the economy and society. Given that, special significance is attributed to the use of labor market data in order to make academic decisions. The integration of IT, big data, and artificial intelligence methods into the decision-making process enhances this efficiency and reduces costs. However, suggested approach requires the training of data specialists and university administrators. The novelty of the research lies in the implementation of machine learning methods, which enable the classification and specification of sets of skills revealed through the analysis of open vacancies for data specialists. This is achieved through clusterization (unsupervised learning) and the development of a model for predicting possible areas of professional growth based on existing skills using the XGBoost method (supervised learning). The developed model possesses high accuracy (99.53%), which means it can be implemented on real data (students) to enhance the mastery of skills recommended based on machine learning. The results of structuring vacancies according to the most relevant skills (5 clusters have been revealed) are relevant for improving individual educational trajectories of academic programs for future data specialists. The article presents an example of the implementation of the created model for specifying elective courses in the academic program “Information Systems and Technologies” for bachelor's degree students at Kherson State University. Although, the implementation of models of machine learning and data-driven decisions significantly depends on data, the results of the research can be implemented in various educational fields.