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Survey of the Different Type of Data Analytics Algorithms

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  • Skylar Hernandez

    (Boeing, Charleston, SC, USA)

Abstract

From as early as 1854 to today, society has been gathering, processing, transforming, modeling and visualizing data to help drive data-driven decisions. The qualitative definition of big data can be defined more conclusively as data that has high volume, velocity, and variety. Whereas, the quantitative definition of big data does vary with respect to time due to the dependence of the time's technology and processing capabilities. However, making use of that big data to facilitate data-driven decisions, one should employ either descriptive, predictive, or prescriptive analytics. This article has discussed and summarized the advantages and disadvantages of the algorithms that fell under descriptive and predictive analytics. Given the sheer number of the different types of algorithms and the amount of versatile data mining software available sometimes, the best big data analytics results can come from mixing two to three of the mentioned algorithms.

Suggested Citation

  • Skylar Hernandez, 2017. "Survey of the Different Type of Data Analytics Algorithms," International Journal of Strategic Information Technology and Applications (IJSITA), IGI Global, vol. 8(1), pages 50-64, January.
    • Handle: RePEc:igg:jsita0:v:8:y:2017:i:1:p:50-64
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