Teaching the Big Scientific Data Analysis

The contemporary Human activity utilizes huge volumes of digital data to solve efficiently multiple socio-economic, scientific and technical problems. Now the big data analysis is mainly oriented to the socioeconomic sphere with a goal to lift the profit. The science and technology to penetrate deeper in the nature of objects and systems under investigation prefer to limit the analysis area, concentrating, for example, only on properties of extra pure materials or isolated systems. In science the analysis should be convergent and the initial data may be and should be regularized to diminish the input data errors. The clusters now are considered as a new or still unknown state of matter. The big thermophysical data analysis appears as the most informative way to discover the properties of clusters in pure real gases, because the continuous spectrum of bound states in clusters prevents from the spectroscopic way for clusters' properties evaluation. The goal of the paper is to teach the main principles of the scientific regular data convergent analysis basing on the author's experience to extract clusters' properties in pure real gases from regularized experimental thermophysical big data. [For the full proceedings, see ED623149.]