Book Image

Practical Big Data Analytics

By : Nataraj Dasgupta
Book Image

Practical Big Data Analytics

By: Nataraj Dasgupta

Overview of this book

Big Data analytics relates to the strategies used by organizations to collect, organize, and analyze large amounts of data to uncover valuable business insights that cannot be analyzed through traditional systems. Crafting an enterprise-scale cost-efficient Big Data and machine learning solution to uncover insights and value from your organization’s data is a challenge. Today, with hundreds of new Big Data systems, machine learning packages, and BI tools, selecting the right combination of technologies is an even greater challenge. This book will help you do that. With the help of this guide, you will be able to bridge the gap between the theoretical world of technology and the practical reality of building corporate Big Data and data science platforms. You will get hands-on exposure to Hadoop and Spark, build machine learning dashboards using R and R Shiny, create web-based apps using NoSQL databases such as MongoDB, and even learn how to write R code for neural networks. By the end of the book, you will have a very clear and concrete understanding of what Big Data analytics means, how it drives revenues for organizations, and how you can develop your own Big Data analytics solution using the different tools and methods articulated in this book.
Table of Contents (16 chapters)
Title Page
Packt Upsell
Contributors
Preface

Machine learning, statistics, and AI


Machine learning is a term that has various synonyms - names that are the result of either marketing activities by corporates or just terms that have been used interchangeably. Although some may argue that they have different implications, they all ultimately refer to machine learning as a subject that facilitates the prediction of future events using historical information.

The commonly heard terms for machine learning include predictive analysis, predictive analytics, predictive modeling, and many others. As such, unless the entity that publishes material explaining their interpretation of the term and more specifically, how it is different, it is generally safe to assume that they are referring to machine learning. This is often a source of confusion among those new to the subject, largely due to the misuse and overuse of technical verbiage.

Statistics, on the other hand, is a distinct subject area that has been well known for over 200 years. The word...