The Role of Mathematics and Statistics in the Field of Data Science and its Application

Mathematics is the rock-solid foundation of everything that happens when science is present, and it is also extremely important in the field of data science since mathematical ideas assist discover models and facilitate the development of algorithms. But, the concepts they present and the tools they enable are the only reasons statistics and arithmetic are so crucial to data science. There is a particular type of mathematical reasoning that is necessary to grasp data, beyond the fundamentals of calculus, discrete mathematics, and linear algebra. For the implementation of such algorithms in data science, a thorough understanding of the various principles of probability and statistics is essential. Machine learning is one of the many modern data science techniques that has a strong mathematical base. The evidence presented in this chapter backs up our earlier claim that math and statistics are the fields that offer the greatest tools and approaches for extracting structure from data. For newcomers coming from other professions to data science, math proficiency is crucial. 

Keywords: Applications in medical science, Bayes’ theorem, Binomial, Bernoulli, Computer vision, Calculus, Calculus in machine learning, Gaussian normal, Linear algebra, Loss function, Mean squared error, Mean absolute error, Nonparametric statistical methods, Regression.