Book Image

Statistics for Data Science

Book Image

Statistics for Data Science

Overview of this book

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. 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. By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically.
Table of Contents (19 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Summary


In this chapter, we said that, currently, how data science is defined is a matter of opinion. A practical explanation is that data science is a progression or, even better, an evolution of thought, consisting of collecting, processing, exploring, and visualizing data, analyzing (data) and/or applying machine learning (to the data), and then deciding (or planning) based upon acquired insight(s).

Then, with the goal of thinking like a data scientist, we introduced and defined a number of common terms and concepts a data scientist should be comfortable with.

In the next chapter, we will present and explain how a data developer might understand and approach the topic of data cleaning using several common statistical methods.