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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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Concepts
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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

Who this book is for

The book is intended for a diverse range of audience. In particular, readers who are keen on understanding the concepts of big data, data science and/or machine learning at a holistic level, namely, how they are all inter-related will gain the most benefit from the book.

Technical audience: For technically minded readers, the book contains detailed explanations of the key industry tools for big data and machine learning. Hands-on exercises using Hadoop, developing machine learning use cases using the R programming language, building comprehensive production-grade dashboards with R Shiny have been covered. Other tutorials in Spark and NoSQL have also been included. Besides the practical aspects, the theoretical underpinnings of these key technologies have also been explained.

Business audience: The extensive theoretical and practical treatment of big data has been supplemented with high level topics around the nuances of deploying and implementing robust big data solutions in the workplace. IT management, CIO organizations, business analytics and other groups who are tasked with defining the corporate strategy around data will find such information very useful and directly applicable.

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