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Practical Big Data Analytics

Practical Big Data Analytics

By : Nataraj Dasgupta
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Practical Big Data Analytics

Practical Big Data Analytics

5 (1)
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 (13 chapters)
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Enterprise Data Science

We have thus far discussed various topics regarding both data mining and machine learning. Most of the examples shown were designed so that anyone with a standard computer would be able to run them and complete the exercises. In real-world situations, datasets would be much larger than those encountered in general home use.

Traditionally, we have relied on well-known database technologies such as SQL Server, Oracle, and others for organizational data warehouse and data management. The advent of NoSQL and Hadoop-based solutions made a significant change to this model of operation. Although companies were at first reluctant, the popular appeal of these tools became too large to ignore, and today, most, if not all, large organizations leverage one or more non-traditional contemporary solution for their enterprise data requirements.

Furthermore, the advent...

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