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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

A roadmap to enterprise analytics success


In our experience, analytics, which is a fairly recent term compared to well-established terms such as data warehouse and others, requires a careful approach in order to ensure both immediate success and the consequent longevity of the initiative.

Projects that prematurely attempt to complete an initial analytics project with large-scale, high-budget engagement run the risk of jeopardizing the entire initiative if the project does not turn out as expected.

Moreover, in such projects, the outcome measures are not clearly defined. In other words, measuring the value of the outcome is ambiguous. Sometimes, it cannot be quantified either. This arises because the success of an analytics initiative has benefits beyond simply the immediate monetary or technical competencies. A successful analytics project often helps to foster executive confidence in the department's ability to conduct said projects, which in turn may lead to bigger endeavors.

The general...

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