Statistics for Data Science

2017 | Computing & IT

Get your statistics basics right before diving in to the world of data science About This Book * No need to take a degree in statistics, read this book and get a strong stats base for data science * If you are scared of statistics and are looking for real-world programs that explain statistics, this book is for you * Learn all about probability, statistics, numerical computations, and more with the help of programs Who This Book Is For This book is intended for those developers who are willing to enter the field of data science and are looking for concise information of statistics with the help of insightful programs and simple explanation. What You Will Learn * Simple statistics, statistical methods that are used in data science algorithms. * The programs and logic for these statistical computations will be clearly explained. * Mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more. * Learn to implement stats in data science tasks such as data cleaning, mining, and analysis. * Learn the required statistics to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks.

* Get comfortable with performing various statistical computations for data science programmatically. In Detail This book will take you through an entire journey of statistics from knowing very little to becoming comfortable in using various statistical methods for data science tasks.This book will start off with simple statistics and then move on to statistical methods that are used in data science algorithms. The programs for these statistical computations will be clearly explained along with the logic. You will come across various mathematical concepts such as variance, standard deviation, probability, matrix calculations,a nd more. You will learn only what is required to implement stats in data science tasks such as data cleaning, mining, and analysis. You will learn required statistics to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks.By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically.

James D. Miller (Author)