How data analysis technologies can help to develop the purpose of big data science and engineering education in the 21st century

Abstract

IBM estimates that 2.5 quintillion bytes of data are created or replicated every day. This is the equivalent of one million hard drives filling up with data every hour. In 2015, data centers occupied the equivalent of nearly 6,000 football fields. By 2020, the amount of digital information is expected to increase exponentially by more than 7 times the volume in 2014. (Desjardins, 2015)

We consider, for practical purposes of discussion in this article, that science is a means of developing explanations of how the natural world works, and engineering is a means of developing solutions to human problems. So we will say that both (science and engineering) are aimed at improving our lives, which represents a strong motivator for the development of a new learning process in which we can give meaning to the analysis of information (data analytics) and the construction of validation models (descriptive, predictive and prescriptive analysis). We will try to develop the following question in the present work: How can analytical data learning techniques be a source of improvement in the quality of education to face the challenges of innovation, competition and productivity in the 21st century?