Statistics for Data Analytics

Statistics is the specific branch of science from where the Data Analysts bring distinct conclusion/interference under the same data. Moving discussion a step further, we shall discuss Important Statistical Techniques like:

Probability

Probability Distributions–Binomial, Poisson & Normal Distribution

Regression Analysis

Correlation Analysis

Index Numbers

Measures of Central Tendency & Dispersion

Statistical Data Analysis

Being a branch of science, Statistics incorporates data acquisition, data interpretation, and data validation, and statistical data analysis is the approach of conducting various statistical operations, i.e. thorough quantitative research that attempts to quantify data and employs some sorts of statistical analysis. Here, quantitative data typically includes descriptive data like survey data and observational data. In the context of business applications, it is a very crucial technique for business intelligence organizations that need to operate with large data volumes. The basic goal of statistical data analysis is to identify trends, for example, in the retailing business, this method can be approached to uncover patterns in unstructured and semi–structured consumer data that can be used for making more powerful decisions for enhancing customer experience and progressing sales. Apart from that, statistical data analysis has various applications in the field of statistical analysis of market research, business intelligence(BI), data analytics in big data, machine learning and deep learning, and financial and economical analysis.