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Practical Big Data Analytics

You're reading from Practical Big Data Analytics Hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R

Product type Paperback

Published in Jan 2018

Publisher Packt

ISBN-13 9781783554393

Length 412 pages

Edition 1st Edition

Languages

Java

Tools

Apache Spark

Concepts

Big Data

Author (1):

Dasgupta

View More author details

Table of Contents (16) Chapters

Title Page

Packt Upsell

Contributors

Preface

1. Too Big or Not Too Big FREE CHAPTER

2. Big Data Mining for the Masses

3. The Analytics Toolkit

4. Big Data With Hadoop

5. Big Data Mining with NoSQL

6. Spark for Big Data Analytics

7. An Introduction to Machine Learning Concepts

8. Machine Learning Deep Dive

9. Enterprise Data Science

10. Closing Thoughts on Big Data

11. External Data Science Resources

1. Other Books You May Enjoy

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R Shiny platform for developers

R Shiny introduced a platform for R developers to create JavaScript-based web applications without having to get involved, or, for that, matter even be proficient in JavaScript.

In order to build our application, we will leverage R Shiny and create an interface to connect to the CMS Open Payments data we set up in the prior section.

If you are using your own R installation (locally), you'll need to install a few R packages. Note that if you are using a Linux workstation, you may need to install some additional Linux packages. For example, in Ubuntu Linux, you'll need to install the following. You may already have some of the packages, in which case you'll receive a message indicating that no further changes were needed for the respective package:

sudo apt-get install software-properties-common libssl-dev libcurl4-openssl-dev gdebi-core rlwrap

Note

If you are using the Packt Data Science VM, you can proceed directly to developing the application as these Linux packages...

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Authors (1)

Dasgupta

Dasgupta

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.

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