Explorando la Ciencia de Datos
Desde la Estadística hasta el Big Data
Keywords:
Data Science, Statistics, CRISP-DM, Big Data, AI
Abstract
Today, Data Science is an interdisciplinary field that involves extracting useful knowledge from large datasets. It focuses on using tools and techniques to collect, process, and analyze data in order to identify patterns, trends, and relationships that can be used to improve decision-making. This article aims to provide a general overview of Data Science and the knowledge it encompasses, offering a comprehensive view of its concepts, methodologies, and Big Data challenges.
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References
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P. Chapman, J. Clinton, R. Kerber, T. Khabaza, T. Reinartz, C. Shearer, and R. Wirth, “CRISP-DM 1.0: Step-by-step data mining guide,” SPSS Inc., 2000.
Dangeti, P. (2017). Statistics for Machine Learning. Packt Publishing.
N. Marz and J. Warren, Big Data: Principles and Best Practices of Scalable Real-Time Data Systems. Shelter Island, NY, USA: Manning Publications, 2015.
V. Mayer-Schönberger and K. Cukier, Big Data: A Revolution That Will Transform How We Live, Work, and Think. Boston, MA, USA: Houghton Mifflin Harcourt, 2013
Information Resources Management Association, Big Data: Concepts, Methodologies, Tools, and Applications. Hershey, PA, USA: IGI Global, 2016.
Genís Roca y Albert Solana, “Big Data para directivos”, Editorial Empresa Activa, 2019.
Juan Gabriel Gomila Salas, Kirill Eremenko, y otros, “Inteligencia Artificial aplicada a Negocios”, Editorial Kindle, 2023.
Published
2024-12-10
How to Cite
Quiroz Fabián, J. L., Pérez Espinosa, A., Román Alonso, G., Castro García, M. A., & Aguilar Cornejo, M. (2024). Explorando la Ciencia de Datos: Desde la Estadística hasta el Big Data. Contactos, Revista De Educación En Ciencias E Ingeniería, (137), 115 - 121. Retrieved from https://contactos.izt.uam.mx/index.php/contactos/article/view/450
Section
Artículos
