The book has been written to cover the basics of analytics before moving to big data and its analytics. It seeks to translate the theory behind big data into principles and practices for a data analyst.

Description

Big Data Analytics presents a comprehensive treatment of the subject for undergraduate and postgraduate students of computer science and engineering, information technology, and other related disciplines.

The book has been written to cover the basics of analytics before moving to big data and its analytics. It seeks to translate the theory behind big data into principles and practices for a data analyst.

The text is categorized into 4 sections:

• Basics of big data and NoSQL systems
• Tools and frameworks for handling big data
• Theory and methods of big data analytics
• Infrastructure for big data
• 
  

• Chapter outlines and learning outcomes listed at the start of each chapter
• Illustrative discussion on big data frameworks and infrastructure
• Algorithms for data analytics on big data frameworks and tools
• Solved numerical examples to supplement the text
• Practice exercises and codes for various case studies on Hadoop, R, Spark, MongoDB, Storm, and Neo4j
• Interview questions highlighted as boxed items in each chapter
• Point-wise summary at the end of each chapter to enable quick revision
• Chapter-end exercises comprising objective-type questions with answers, critical thinking questions, descriptive type questions, and numerical exercises

table of contents

1. Introduction to Big Data Analytics
2. Data Analytics Life Cycle
3. Introduction to R
4. NoSQL
5. Hadoop
6. Introduction to Preprocessing
7. Theory and Methods: Association Rules
8. Theory and Methods: Clustering
9. Regression
10. Classification
11. Time Series Analysis
12. Theory and Methods—Text Analysis
13. Mining Data Streams
14. NoSQL Databases—Neo4j and MongoDB
15. Big Data Technology and Tools—Spark and Storm
16. Big Data Infrastructure