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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

The human factor


The significant advantages of big data and data science notwithstanding, their successes and breakthrough growth, it is still important to bear in mind that the element of human thinking is essential in all endeavors.

Big data technologies will allow us to analyze data more efficiently. But we still need to use proper judgment to decide on our ideal use cases. This is not trivial. Large companies find initiatives just as challenging (although at a higher scale) as seasoned big data professionals.

Similarly, data science and machine learning can empower us to make predictions and gain foresight with the help of sophisticated algorithms and code. However, it is still incumbent upon the user to evaluate the results and make decisions not solely based on the predicted output. Users should apply common sense and experience in making such assessments. If the GPS instructs the driver to go on a certain road on a snowy winter night and the driver knows that the road won't have been...

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