Statistics for Data Science

Statistics for Data Science"margin top: 0. 6em; margin bottom: 1. 2em; padding: 0px; color: rgb(73, 73, 73); font family: Ubuntu, sans serif; background color: rgb(243, 247, 248);"Data science is an ever evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on."margin top: 0. 6em;

"margin-top: 0.6em; margin-bottom: 1.2em; padding: 0px; color: rgb(73, 73, 73); font-family: Ubuntu, sans-serif; background-color: rgb(243, 247, 248);"Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on."margin-top: 0.6em; margin-bottom: 1.2em; padding: 0px; color: rgb(73, 73, 73); font-family: Ubuntu, sans-serif; background-color: rgb(243, 247, 248);"This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks. the end of the book, you will be comfortable with performing various statistical computations for data science programmatically.