<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In the presence of massive data coming with high heterogeneity we need to change our statistical thinking and statistical education in order to adapt both - classical statistics and software developments that address new challenges. Significant developments include open data, big data, data visualisation, and they are changing the nature of the evidence that is available, the ways in which it is presented and the skills needed for its interpretation. The amount of information is not the most important issue – the real challenge is the combination of the amount and the complexity of data. Moreover, a need arises to know how uncertain situations should be dealt with and what decisions should be taken when information is insufficient (which can also be observed for large datasets). In the paper we discuss the idea of computational statistics as a new approach to statistical teaching and we try to answer a question: how we can best prepare the next generation of statisticians.</p></abstract>

In the presence of massive data coming with high heterogeneity we need to change our statistical thinking and statistical education in order to adapt both - classical statistics and software developments that address new challenges. Significant developments include open data, big data, data visualisation, and they are changing the nature of the evidence that is available, the ways in which it is presented and the skills needed for its interpretation. The amount of information is not the most important issue – the real challenge is the combination of the amount and the complexity of data. Moreover, a need arises to know how uncertain situations should be dealt with and what decisions should be taken when information is insufficient (which can also be observed for large datasets). In the paper we discuss the idea of computational statistics as a new approach to statistical teaching and we try to answer a question: how we can best prepare the next generation of statisticians.

Statistical Computing in Information Society

University of Lodz, Faculty of Economics and Sociology, Institute of Statistics and Demography, POW 3/5, 90-255 Łódź, Poland

Centre of Mathematical Statistics, Statistical Office in Łódź Suwalska 29, 93-176 Lódź, Poland

Folia Oeconomica Stetinensia

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Statistical Computing in Information Society"}

In the presence of massive data coming with high heterogeneity we need to change our statistical thinking and statistical education in order to adapt both -...

Statistical Computing in Information Society

Publié en ligne: 30 avr. 2016

Pages: 144 - 152

Reçu: 20 mai 2015

Accepté: 03 déc. 2015

DOI: https://doi.org/10.1515/foli-2015-0041

Mots clés
statistical education, statistical computing, big data, information society

© 2015 University of Szczecin

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Statistical Computing in Information Society

Publié en ligne: 30 avr. 2016

Pages: 144 - 152

Reçu: 20 mai 2015

Accepté: 03 déc. 2015

DOI: https://doi.org/10.1515/foli-2015-0041

Mots clésstatistical education, statistical computing, big data, information society

© 2015 University of Szczecin

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
statistical education, statistical computing, big data, information society