Practical Big Data Analytics

Practical Big Data Analytics


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 otherwise 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 with the practical ground 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 different tools and methods articulated in this book.

What You Will Learn

  • Get a 360-degree view into the world of Big Data, data science and machine learning
  • Broad range of technical and business Big Data analytics topics that caters to the interests of the 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 and 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

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