You're reading from Big Data Analytics with Java Data analysis, visualization & machine learning techniques

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781787288980

Length 418 pages

Edition 1st Edition

Languages

Java

Tools

Apache Spark

Concepts

Big Data

Author (1):

RAJAT MEHTA

View More author details

Table of Contents (15) Chapters

Preface

1. Big Data Analytics with Java FREE CHAPTER

2. First Steps in Data Analysis

3. Data Visualization

4. Basics of Machine Learning

5. Regression on Big Data

6. Naive Bayes and Sentiment Analysis

7. Decision Trees

8. Ensembling on Big Data

9. Recommendation Systems

10. Clustering and Customer Segmentation on Big Data

11. Massive Graphs on Big Data

12. Real-Time Analytics on Big Data

13. Deep Learning Using Big Data

Index

Preface

Even as you read this content, there is a revolution happening behind the scenes in the field of big data. From every coffee that you pick up from a coffee store to everything you click or purchase online, almost every transaction, click, or choice of yours is getting analyzed. From this analysis, a lot of deductions are now being made to offer you new stuff and better choices according to your likes. These techniques and associated technologies are picking up so fast that as developers we all should be a part of this new wave in the field of software. This would allow us better prospects in our careers, as well as enhance our skill set to directly impact the business we work for.

Earlier technologies such as machine learning and artificial intelligence used to sit in the labs of many PhD students. But with the rise of big data, these technologies have gone mainstream now. So, using these technologies, you can now predict which advertisement the user is going to click on next, or which product they would like to buy, or it can also show whether the image of a tumor is cancerous or not. The opportunities here are vast. Big data in itself consists of a whole lot of technologies whether cluster computing frameworks such as Apache Spark or Tez or distributed filesystems such as HDFS and Amazon S3 or real-time SQL on underlying data using Impala or Spark SQL.

This book provides a lot of information on big data technologies, including machine learning, graph analytics, real-time analytics and an introductory chapter on deep learning as well. I have tried to cover both technical and conceptual aspects of these technologies. In doing so, I have used many real-world case studies to depict how these technologies can be used in real life. So this book will teach you how to run a fast algorithm on the transactional data available on an e-commerce site to figure out which items sell together, or how to run a page rank algorithm on a flight dataset to figure out the most important airports in a country based on air traffic. There are many content gems like these in the book for readers.