An Algorithm for Exploratory Analysis and Normalization of Big Data with Pandas

Authors

  • Mariya Zhekova Department of Computer Systems and Technologies, University of Food Technology, Bulgaria

DOI:

https://doi.org/10.7546/CRABS.2023.11.09

Keywords:

big data, data mining, big data analysis, educational analytics, natural language processing

Abstract

The digitization of business processes and the extraction of answers to user requests for big data are modern problems that are of great interest to scientists and researchers. The data generated so far, located in various corpora, is much more than can be analyzed. Therefore, they are collected, identified, cleaned and normalized to be used most adequately. Segmentation, assumptions and hypotheses contribute to the degree of satisfaction with the returned result. The research proposed a general method for collecting, cleaning and normalizing data from various sources, structurally modelling it into appropriate models, then testing hypotheses and analyzing the obtained results to conclude large academic data that will benefit the business in making management decisions. This is possible with the means of computational linguistics and with the help of Python data manipulation libraries.

Author Biography

Mariya Zhekova, Department of Computer Systems and Technologies, University of Food Technology, Bulgaria

Mailing Address:
Department of Computer Systems and Technologies,
University of Food Technology
26 Maritsa Blvd
4002 Plovdiv, Bulgaria

E-mail: m_jekova@uft-plovdiv.bg

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Published

27-11-2023

How to Cite

[1]
M. Zhekova, “An Algorithm for Exploratory Analysis and Normalization of Big Data with Pandas”, C. R. Acad. Bulg. Sci. , vol. 76, no. 11, pp. 1716–1723, Nov. 2023.

Issue

Section

Engineering Sciences