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Key benefits

• Acquire real-world set of tools for building enterprise level data science applications

• Surpasses the barrier of other languages in data science and learn create useful object-oriented codes

• Extensive use of Java compliant big data tools like apache spark, Hadoop, etc.

Description

This book covers case studies such as sentiment analysis on a tweet dataset, recommendations on a movielens dataset, customer segmentation on an ecommerce dataset, and graph analysis on actual flights dataset. This book is an end-to-end guide to implement analytics on big data with Java. Java is the de facto language for major big data environments, including Hadoop. This book will teach you how to perform analytics on big data with production-friendly Java. This book basically divided into two sections. The first part is an introduction that will help the readers get acquainted with big data environments, whereas the second part will contain a hardcore discussion on all the concepts in analytics on big data. It will take you from data analysis and data visualization to the core concepts and advantages of machine learning, real-life usage of regression and classification using Naïve Bayes, a deep discussion on the concepts of clustering,and a review of simple neural networks on big data using deepLearning4j or plain Java Spark code. This book is a must-have book for Java developers who want to start learning big data analytics and want to use it in the real world.

What you will learn

• Start from simple analytic tasks on big data

• Get into more complex tasks with predictive analytics on big data using

machine learning

• Learn real time analytic tasks

• Understand the concepts with examples and case studies

• Prepare and refine data for analysis

• Create charts in order to understand the data

• See various real-world datasets

What do you get with a Packt Subscription?

Free for first 7 days. $19.99 p/m after that. Cancel any time!

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Pach P Aug 25, 2017

The best part about this book is that it covered the applicability of Big Data Analytics from multiple perspectives in very simple, easy to understand format. This allowed me to 'build a big picture view through hands on approach' on how to effectively leverage different Machine Learning concepts & APIs in Big Data Analytics.Each of the subjects was covered with clear and concise case studies and examples which I could readily take from github and tinker around. This was specially helpful and relevant to me as a relative newbie in this field who has tried making sense of it all by walking through the maze of blogs, api docs and videos.Were it basics on Big Data, Visualization, Regression, Sentiment Analytics or invaluable coverage on more advanced subjects like Graph Analytics, Recommendation Engines, Real Time Analytics and Deep Learning - easy readability and relatable examples helped me flow through the subjects and progressively build a deeper understanding of how these different ideas connect together.Hats off to the author for having taken the pains of cutting through the clutter and delivering the powerful concepts in elegantly simple examples. His efforts resulted in this deceptively simple yet comprehensive masterpiece which I could read and follow along from start to finish in about 6 weeks. Wish I had come across this book 2 years ago..

Amazon Verified review

Vladimir Alarcon Jan 09, 2020

**The Bad**80% of the code examples, JSON examples, and other don't pass the most basic syntax. Seriously. I don't know if the examples are intentionally badly written but they won't compile, not even close. The author talks about getting the code from some web site; maybe they work there, but don't expect to understand the example just by reading the book.The English writing skills are decent, though sometimes simple, if you know what I mean. I don't think it's OK to say "this is bad". The authors should phrase it better by saying something like "this won't work well in this scenario, or won't scale well in the future, and/or won't show the correct result all the time". Some personal opinions are phrased as facts without no further explanation... and some readers may think they are facts. Did I mention there are many misspellings?**The Good**The content. The book covers a fairly amount of content and buzzwords you have heard, and it explains it well, albeit sometimes in a too "simple" (read confusing) manner. It's fairly complete.A decent developer will be able to try each example 5 or 10 times to finally make it work, changing the syntax, guessing where to get the missing libraries, removing/adding some lines, figuring out imports, you-name-it. I found that this actually adds to the experience, so I ended up learning a lot with all that hands-on experimentation. However, an entry-level developer may not agree on this one.**Bottom Line**The book covers a lot of content you probably want to learn. It does explains those buzzwords pretty well. Finally, if you are a hands-on Java developer, you can squeeze knowledge from this book.

alf_biker Nov 12, 2017

The book is good and covers a lot of stuff, but the code examples don't work. I think with a little more work it can be excellent.

Big Data Analytics with Java: Data analysis, visualization & machine learning techniques

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