Tools Used By Data Science

These “questions” are the tools that data science uses. Data science or Data Science is based on three tools: programming, mathematics, and statistics; and experience in the field of study according to the data science certification course in Hyderabad.

The Programing

Large masses of data can only be handled from a (powerful) computer, and, therefore, the language of communication between humans and big data is computer programming. Imagine an “Excel” table with 850,000 rows and 500 columns, to mention a minimal example from Big Data.

A realistic example of massive data could be all the institutes in a country: the number of students, their gender, age, grades, and attendance. They can be data of a different nature, and that cannot be structured or adapted to a table such as we understand them.

The Mathematics

To order, process, and analyze these overlapping layers of information, multiple mathematical approaches are used that seek to reduce the complexity of the data without losing information. Formulas and algorithms are applied to the data, with the idea of removing all the information that is not necessary for the “question” we are asking. In this way, the patterns appear, and the answers converge at one point.

Returning to the example that we put before the massive data of the institutes of a country if we applied filters and algorithms to stay only with the information of the grades obtained by the students and absenteeism, and we “asked” the data if there is a relationship Between the two variables (grades and absenteeism), we would see that one of the variables (grades) seems to depend on the other (absenteeism). The result of this analysis would be that the two variables are related.

The Experience In The Field

The cornerstone of data science is that the data scientist has a broad understanding of the field of study. If not, many conclusions would be drawn about the data that, without knowledge of the field of study, would be wrong. Following our example of high school students’ data, when analyzing in detail and with knowledge in the field, we would see that all of them have at least 28% absenteeism per week! Regardless of the marks obtained.

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