Practical Big Data Analytics: Hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R - Couverture souple

Dasgupta, Nataraj

Couverture souple

ISBN 10 : 1783554398 ISBN 13 : 9781783554393

Editeur : Packt Publishing, 2018

Synopsis

Get command of your organizational Big Data using the power of data science and analytics

Key Features

• A perfect companion to boost your Big Data storing, processing, analyzing skills to help you take informed business decisions
• Work with the best tools such as Apache Hadoop, R, Python, and Spark for NoSQL platforms to perform massive online analyses
• Get expert tips on statistical inference, machine learning, mathematical modeling, and data visualization for Big Data

Book Description

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.

What you will learn

• Get a 360-degree view of the world of Big Data, data science, and machine learning
• Go through a broad range of technical and business Big Data analytics topics that caters to the interests of technical experts as well as corporate IT executives
• Get hands-on experience with industry-standard Big Data and machine learning tools such as Hadoop, Spark, MongoDB, kdb+, and R
• Create production-grade machine learning BI dashboards using R and R Shiny with step-by-step instructions
• Learn how to combine open-source Big Data, machine learning, an BI tools to create low-cost business analytics applications
• Understand corporate strategies for successful Big Data and data science projects
• Go beyond general-purpose analytics to develop cutting-edge Big Data applications using emerging technologies

Who this book is for:

The book is intended for existing and aspiring Big Data professionals who wish to become the go-to person in their organization when it comes to Big Data architecture, analytics, and governance. While no prior knowledge of Big Data or related technologies is assumed, it will be helpful to have some programming experience.

A propos du livre

À propos de l?auteur

Nataraj Dasgupta is the vice president of advanced analytics at RxDataScience Inc. Nataraj has been in the IT industry for more than 19 years, and has worked in the technical and analytics divisions of Philip Morris, IBM, UBS Investment Bank, and Purdue Pharma. At Purdue Pharma, Nataraj led the data science division, where he developed the company's award-winning big data and machine learning platform. Prior to Purdue, at UBS, he held the role of Associate Director, working with high-frequency and algorithmic trading technologies in the foreign exchange trading division of the bank.

Détails de l'édition

• Éditeur: Packt Publishing
• Date d'édition: 2018
• Langue: anglais
• ISBN 10: 1783554398
• ISBN 13: 9781783554393
• Reliure: Broché
• Nombre de pages: 412
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible