Modern challenges force us the employ new technologies in the educational process, which are inextricably linked with data. Through analytics in 80% of cases it is possible to predict student drop out six months in advance. This allows higher education institutions to intervene on an individual level and offer advice to the students who need it most. Generalising the data it is possible to identify problematic courses, dysfunctional departments or user cohorts. Student retention is cost-effective for both individuals and institutions. During the session, we will also discuss hybrid learning technologies that enable you to save and process the content of face-to-face lectures and seminars, the use of neurotechnology to increase the effectiveness of online education, and teaching methods for working with big data for scientific, educational and commercial purposes.