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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

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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

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Installing Python

We proceed with the installation as follows:

Similar to R, Python has gained popularity due to its versatile and diverse range of packages. Python is generally available as part of most modern Linux-based operating systems. For our exercises, we will use Anaconda from Continuum Analytics®, which enhances the base open source Python offering with many data-mining- and machine-learning-related packages that are installed natively as part of the platform. This alleviates the need for the practitioner to manually download and install packages. In that sense, it is conceptually similar in spirit to Microsoft R Open. Just as Microsoft R enhances the base open source R offering with additional functionality, Anaconda improves upon the offerings of base open source Python to provide new capabilities.

Steps for installing Anaconda Python
Go to https://www.continuum.io/downloads:

Python Anaconda Homepage

Download the distribution that is appropriate for your system. Note that we'll be...

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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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