Data science: an emerging discipline

The role of data scientist has been described as the “sexiest job of the 21st Century”. While possibly there is a degree of hype associated with such a claim, there are factors at play such as the unprecedented growth in the amount of data being generated. This paper characterises the already established disciplines which underpin data science, viz., data engineering, statistics, and data mining. Following a characterisation of the previous fields, data science is found to be most closely related to data mining. However, in contrast to data mining, data science promises to operate over datasets that exhibit significant challenges in terms of the four Vs: Volume, Variety, Velocity and Veracity. This paper notes that the current emphasis, both in industry and academia, is on the first three Vs, which pose mainly scientific or technological challenges, rather than Veracity, which is a truly scientific (and arguably a more complex) challenge. Data Science can be seen to have a more ambitious objective than what traditionally data mining has: as a science, data science aims to lead to the creation of new theories and knowledge. This paper notes that, ironically, the veracity dimension, which is arguably the closest one relating to this objective, is being neglected. Despite the current media frenzy about data science, the paper concludes that more time is needed to see whether it will emerge as discipline in its own right.

• Autores: Galpin, Ixent
• Fecha de publicación: 2016
• Institución: Universidad Santo Tomás
• Repositorio: Repositorio Institucional USTA
• OAI Identifier: oai:repository.usta.edu.co:11634/11508
• Acceso en línea: http://hdl.handle.net/11634/11508
• Palabra clave: Data science; Data mining; Data engineering; Big Data
• License: http://purl.org/coar/access_right/c_abf2