Analysis of the Financial Times ranking "master in management" with machine learning

University rankings play nowadays a major role in the decision of many students with regards to their future schools. Nonetheless, these rankings often remain quite opaque: not all data are made available, the methodology behind the rankings is not well defined, etc. One of the main ranking centred on business schools is the "Master in Management" from the Financial Times. This work aims to study the relevance of this ranking and its possible flaws. Several techniques are conducted, as a robustness analysis to assess the sturdiness of the ranking when facing uncertainties or dimensionality reductions to visualize the data set in two or three dimensions and thus envision local neighbourhoods of schools. Finally, from these analyses, potential improvements to the ranking from the Financial Times are discussed, from the design of a new ranking from scratch to the addition of visualization tools to enhance the informative character of the ranking.