Practical Big Data Analytics

Description

Hands-on techniques to implement enterprise analytics and machine learning using Hadoop, Spark, NoSQL and R

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

1. Too Big Or Not Too Big
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. Appendix- External Data Science Resources

Author Biography

Nataraj Dasgupta is the vice president of advanced analytics at RxDataScience Inc. Nataraj has been in the IT industry for more than 19 years, and has worked in the technical and analytics divisions of Philip Morris, IBM, UBS Investment Bank, and Purdue Pharma. At Purdue Pharma, Nataraj led the data science division, where he developed the company’s award-winning big data and machine learning platform. Prior to Purdue, at UBS, he held the role of Associate Director, working with high-frequency and algorithmic trading technologies in the foreign exchange trading division of the bank.