Role of Statistical Methods in Data Science

Abstract

Data science is a multidisciplinary field that lets you extract knowledge from structured or unstructured data. It allows you to transform between a business and research problem and then revert to a practical solution. Everyday life applications result in voluminous data which must be suitably reduced so that the same can be retrieved easily and can be used for further analysis. Statistics is the most critical unit of data science. You can’t solve real-world problems with data science/machine learning if you don’t have a good grip on statistical fundamentals. The role of statistical methods in data science is to function as a tool to analyze the data and draw conclusions from them. Statistical methods serve as a foundation while dealing with such data and its analysis in data science. There are certain fundamental concepts and basics which must be acquired before jumping into advanced algorithms. The motive behind this chapter is to embrace an extensive picture of the fundamentals of statistical methods that will induce your journey to data science. We are going to use Python pandas for statistical data analysis, using data stored as data frames. The next task to perform here will be preliminary stages like analyzing data in staging, cleaning, and transformation of data in preparation for analysis and techniques for statistical data modeling using different advanced functions available in Python libraries. This will incorporate fitting your data to the statistical functions like probability distribution functions and computing associations among different variables using different models.