A data analytics approach for university competitiveness: the QS rankings

In recent years, higher education has been facing the entrance to the internationalmarket due to globalization, this has developed a highly competitive environment, in whichmany institutions have used university rankings as a tool to attract the best academic andstudent talent from all over the world. In this work we take as a base the ranking of QSWord University Rankings and QS Best Student Cities, to apply data science techniques.Extract information on the performance of the most attractive institutions and cities forstudents worldwide, and develop a methodology that allows the stakeholders of the insti-tutions and cities to improve their services for the benefit of students interested in receivingan education of global quality. We accumulated ten years of university rankings (2011-2020) and six years of city rankings (2014-2019), we carried out an exploratory analysisof the indicators and their influence with the final score, later we trained a multiple regres-sion model and panel data to make predictions in the score. Finally, in order to predictthe position, we carry out groupings and train various machine learning algorithms. Withthis work we show a methodology that allows administrators to plan long-term institutionalimprovements to offer a better education and improve their performance in world rankings.