During a global crisis, leaders around the world need to adapt fast. In these crucial moments, managing people effectively is the most important part, because through communication and collaboration situations can usually be overcome. This paper aims to highlight the two global crises faced by the education system in the 21st century: the global crisis due to the low level of development of some countries and the coronavirus pandemic. The paper summarizes the situation Sub-Saharan Africa regions have faced in the last 20 years, highlighting the need for a system that includes rules on: gender equality, equity, inclusion, and access to education for everybody. Moreover, the ongoing COVID-19 pandemic should also be analyzed in order to exemplify the importance of data collection by statistical institutions. The central research illustrates the importance of managing people during a global crisis, focusing on the rules and actions that must be taken into account when the educational system collapses. All of the aforementioned aspects of the paper emphasize the benefits brought by predictive analytics and how it can improve the educational system.

Nowadays, the renewable energy sector is an area of interest for every state. Global regulations and policies encourage the development of these technologies, given the current political context, but also environmental issues. Romania, due to its geographical position and climate, is considered a country with high potential regarding the implementation of alternative sources of renewable energy. This research presents the importance of solar energy and provides a statistical analysis on the sectors influencing the implementation of green energy. At the same time, those counties that are eligible are identified and different scenarios are created for the ineligible counties that lead to their eligibility. The research develops 3 main objectives. To begin with, it is desired to be created an overview of the indicators included in the analysis, in order to develop a detailed statistical analysis of the situation of each county of Romania. Following this extracted information, the second objective is outlined, which is to create an indicator that groups counties into counties eligible for solar energy and counties ineligible for solar energy using the K-Means Cluster-unsupervised learning algorithm. Finally, using the supervised learning algorithm - Logistic Regression, predictions will be made with the help of which those sectors of activity that can be improved in order to implement green energy will be identified.