Tools used in the Life Cycle of Data Science and their Features

Abstract

As a result of digitization, data sets are fast expanding in various ways. When there are enormous amounts of data or information groups that are complicated in nature, typical data processing methods are unable to handle them. Researchers, scientists, businesses, government entities, advertising companies, and medical researchers all face more challenges when collecting and analyzing for decision-making. The data that is accessible for research must be analyzed utilizing a variety of data analytics methods. These solutions aid in dealing with large volumes of unstructured, organized, or semi-structured data material that is constantly changing and impossible to process using traditional database management tools. This paper goes over the most common uses of data analysis tools, as well as their characteristics towards data validation. This paper presents a review and analysis of various existing ways to supporting data analysis tools for users, with a focus on identifying critical qualities. Weaknesses, opportunities, and uses will be examined in future research.