The quantitative study is the procedure for gathering and processing numerical information.. Quantitative data analysis involves examining number-based and numerical data using various statistical methods. To explain behavior, quantitative analysis entails mathematical and statistical modeling, evaluation, and study (Bloomfield & Fisher, 2019). Quantitative analysts quantify a situation by assigning a numerical value to it. Both inferential and descriptive statistics are used in the data analysis. Quantitative inferential statistics are used to conclude by generalizing the results that are obtained in a random sample. Descriptive data analysis in quantitative research generally describes the construction of tables of means and quantiles and measurements of dispersion like variance that can be used to investigate many different hypotheses.

The type of test applied in the quantitative analysis depends on the types of variables used. Variuos quantitative tests are done in quantitative research. Quantitative tests make it possible to observe the behavior (Larson‐Hall & Plonsky,2015). The tests include the testing methods like significance testing, quantitative data tests, and survey. Significance testing in quantitative research consists of applying the statistical tests, mainly unpaired t-tests, and the z- tests. Quantitative data tests are a systematic method for determining whether data sets are significantly different (Gostick et al., 2019. Quantitative user tests can also be used when testing the usability of an application interface.

Commonly used data analysis programs include R and Python. R is a statistical programming language that can be used in statistical analysis. R is a powerful program that can also be used in statistical modeling classification clustering and carrying out statistical tests. R can easily manipulate data sets and produce high graphics of the data Python commonly uses indescriptive analysis. Python in quantitative research can be used in quantitative trading, which involves inventing and implementing trading strategies centered on mathematical and statistical studies (Gostick et al., 2019). The various standard fields where Python can be used for data analysis are data mining, data processing, modeling, and data visualization.