Book Image

R Data Science Essentials

Book Image

R Data Science Essentials

Overview of this book

With organizations increasingly embedding data science across their enterprise and with management becoming more data-driven it is an urgent requirement for analysts and managers to understand the key concept of data science. The data science concepts discussed in this book will help you make key decisions and solve the complex problems you will inevitably face in this new world. R Data Science Essentials will introduce you to various important concepts in the field of data science using R. We start by reading data from multiple sources, then move on to processing the data, extracting hidden patterns, building predictive and forecasting models, building a recommendation engine, and communicating to the user through stunning visualizations and dashboards. By the end of this book, you will have an understanding of some very important techniques in data science, be able to implement them using R, understand and interpret the outcomes, and know how they helps businesses make a decision.
Table of Contents (15 chapters)
R Data Science Essentials
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Index

Chapter 2. Exploratory Data Analysis

Exploratory data analysis is a very important topic in the field of data analysis. It is an approach of analyzing the data and summarizing the main characteristics of the dataset. The main objective of exploratory data analysis is to check various hypotheses in order to get a better understanding about the dataset.

Exploratory data analysis includes many statistical techniques and visual and nonvisual analysis. When your study has to be communicated with peers as well as with other audience with non-data science backgrounds, it is advisable to use a lot of visual techniques that help in better communications.

Some of the expectations out of exploratory data analysis are getting insights out of the data, extracting the important variables in the dataset (depending on the problem to be solved), identifying the outliers in the data, and getting results of various testing hypotheses. These results play a very important role in how to solve the business problems...