Data Science in Practice

In the era of data-driven decision-making, data science plays a crucial role in making efficient and effective decisions in all research fields. This book is a great attempt to efficiently demonstrate the tools in the statistical language R with an extensive collection of examples, error notes, tools for making decisions, and other useful advice. This book offers a comprehensive overview of the concepts and methods used frequently in data science along with in-depth details on the underlying theories, models, and application situations.

This book, which is organized into three main sections, discusses the concept of data science, its applications and contexts, and how to use contemporary open-source software to put it into practice. In Chapters 1 and 2, the authors highlight the concepts of data science, machine learning, artificial intelligence, the necessities of learning R, and data science projects. The names of more programming languages, including Python, Julia, etc., were stated by the author in Chapter 4. However, R installation and its fundamental features occupy almost this entire chapter. Chapter 5 includes R scripts, outputs, and implementations for exploratory data analysis. From Chapters 6–9, several modelling techniques for forecasting, clustering, classification, and association rules are covered together with examples, R programs, and results interpretation that will help you comprehend the theories and applications. These might improve readers’ comprehension of how to manage their own projects.

The writers covered Git, GitHub, and Shiny apps in Chapter 10, which will be helpful for project managers and dashboard presenters of their study results. In Chapter 11, the author masterfully discussed the importance of ethical issues in both academia and industry. The writers did a good job of explaining how to manage error messages, where to find help, and a glossary of this book. This book will enable the reader to analyse their own data for research reasons, which is one of its biggest benefits.

This book is really helpful to decision-makers and data scientists. Therefore, I wholeheartedly and enthusiastically suggest it to students, researchers, and data scientists who work in any field. This book will be a wonderful library collection.