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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

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781783554393
Length 412 pages
Edition 1st Edition
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Author (1):
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Nataraj Dasgupta Nataraj Dasgupta
Author Profile Icon Nataraj Dasgupta
Nataraj Dasgupta
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Table of Contents (13) Chapters Close

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 12. Other Books You May Enjoy

Enterprise data science – machine learning and AI


Data science solutions have matured rapidly over the past 4 - 5 years, similar to the movement in other areas of data science such as NoSQL, Hadoop, and other data mining solutions. Although many of the prior database systems also incorporate key features of data science, such as machine learning and others, this section highlights some of the solutions at a high level that are primarily used for machine learning and/or AI, as opposed to data management.

Indeed, the distinction between Big Data products and data science products has become blurred, since products that were originally intended for Big Data handling have incorporated key features of data science, and vice versa.

The R programming language

R, as we have seen in prior chapters, is an environment originally designed for statistical programming. It emerged out of a project at the University of New Zealand, where Ross Ihanka and Robert Gentleman developed R as a variation of the S...

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