Data science education: a preliminary analysis of the U.S landscape

• Autor(a) principal: Curty, Renata Gonçalves
• Data de Publicação: 2016
• Outros Autores: Serafim, Jucenir da Silva
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Informação & Informação
• Texto Completo: https://ojs.uel.br/revistas/uel/index.php/informacao/article/view/27945
• topic: Data Science; Data Scientist; Professional Skills; Professional Qualification; Ciencia de Datos; Cientista de Datos; Competencias Profesionales; Formación Professional; Ciência de Dados; Cientista de Dados; Competências Profissionais; Formação Profissional

Introduction: Data scientists has received great attention in recent years following the demands of the labor market stimulated by the open science and big data era. Originally widespread in 2008 and, since then, present in many different industries and applications; data science was announced in 2012 as the most attractive and one of the best paid jobs of the century, culminating with an increasing supply of training courses. Objective: Characterize and understand the formative aspects of data scientists. Methodology: This article describes part of a survey research based on analysis of 93 degrees in data science offered by US institutions. Results: The content analysis of the information publicized on the websites of the identified programs provides evidence that this professional is trained to deal with issues related to the collection, treatment, processing, analysis, visualization and curation of large and heterogeneous data collections in order to solving real-life and practical problems. Conclusion: Findings also revealed that, in general, training in science data places great emphasis on statistical skills, mathematics and computing, including programming and advanced modeling, many of which are placed as prerequisites for admission in these programs.