Data Analytics

Abstract

Statistical approaches are critical because before machine learning (ML), statistical approaches (which still form an important part of data analytics today) offered some frameworks for combining methods of analysing data, hypothesis testing and prediction. Although antiquated by today’s standards, these methods form the core of contemporary ML and are still critical to spotting trends, relationships and patterns of interest in your data. In this chapter, we will delve into the statistical approach of data analytics and more particularly talk about the ML toolbox along with a few basic statistical analysis methods. Important sections are data preprocessing, statistical hypothesis testing, regression analysis, classification techniques and advanced statistical methods like Bayesian inference. Furthermore, this chapter shows how statistical methods can complement ML pipelines, and we give examples of their use in various industries.

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