Practical Big Data Analytics
By :
Practical Big Data Analytics
By:
Overview of this book
Big Data analytics relates to the strategies used by organizations to collect, organize, and analyze large amounts of data to uncover valuable business insights that cannot be analyzed through traditional systems. Crafting an enterprise-scale cost-efficient Big Data and machine learning solution to uncover insights and value from your organization’s data is a challenge. Today, with hundreds of new Big Data systems, machine learning packages, and BI tools, selecting the right combination of technologies is an even greater challenge.
This book will help you do that. With the help of this guide, you will be able to bridge the gap between the theoretical world of technology and the practical reality of building corporate Big Data and data science platforms. You will get hands-on exposure to Hadoop and Spark, build machine learning dashboards using R and R Shiny, create web-based apps using NoSQL databases such as MongoDB, and even learn how to write R code for neural networks.
By the end of the book, you will have a very clear and concrete understanding of what Big Data analytics means, how it drives revenues for organizations, and how you can develop your own Big Data analytics solution using the different tools and methods articulated
in this book.
Table of Contents (16 chapters)
Title Page
Packt Upsell
Contributors
Preface
Free Chapter
Too Big or Not Too Big
Big Data Mining for the Masses
The Analytics Toolkit
Big Data With Hadoop
Big Data Mining with NoSQL
Spark for Big Data Analytics
An Introduction to Machine Learning Concepts
Machine Learning Deep Dive
Enterprise Data Science
Closing Thoughts on Big Data
External Data Science Resources
Other Books You May Enjoy
Customer Reviews