IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Tools, Technologies, and Methodologies to Support Data Science: Support Technologies for Data Science

Tools, Technologies, and Methodologies to Support Data Science: Support Technologies for Data Science
View Sample PDF
Author(s): Ricardo A. Barrera-Cámara (Universidad Autónoma del Carmen, Mexico), Ana Canepa-Saenz (Universidad Autónoma del Carmen, Mexico), Jorge A. Ruiz-Vanoye (Universidad Politécnica de Pachuca, Mexico), Alejandro Fuentes-Penna (Centro Interdisciplinario de Investigación y Docencia en Educación Técnica, Mexico), Miguel Ángel Ruiz-Jaimes (Universidad Politécnica de Morelos, Mexico) and Maria Beatriz Bernábe-Loranca (Benemérita Universidad Autónoma de Puebla, Mexico)
Copyright: 2021
Pages: 23
Source title: Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics
Source Author(s)/Editor(s): Bhushan Patil (Independent Researcher, India) and Manisha Vohra (Independent Researcher, India)
DOI: 10.4018/978-1-7998-3053-5.ch004

Purchase

View Tools, Technologies, and Methodologies to Support Data Science: Support Technologies for Data Science on the publisher's website for pricing and purchasing information.

Abstract

Various devices such as smart phones, computers, tablets, biomedical equipment, sports equipment, and information systems generate a large amount of data and useful information in transactional information systems. However, these generate information that may not be perceptible or analyzed adequately for decision-making. There are technology, tools, algorithms, models that support analysis, visualization, learning, and prediction. Data science involves techniques, methods to abstract knowledge generated through diverse sources. It combines fields such as statistics, machine learning, data mining, visualization, and predictive analysis. This chapter aims to be a guide regarding applicable statistical and computational tools in data science.

Related Content

Naciye Güliz Uğur, Aykut Hamit Turan. © 2022. 21 pages.
Richard S. Segall, Gao Niu. © 2022. 32 pages.
Preeti Bala. © 2022. 13 pages.
Dineshkumar Bhagwandas Vaghela. © 2022. 10 pages.
Richard S. Segall. © 2022. 42 pages.
Pijush Kanti Dutta Pramanik, Saurabh Pal, Moutan Mukhopadhyay. © 2022. 29 pages.
Ebru Aydindag Bayrak, Pinar Kirci. © 2022. 15 pages.
Body Bottom