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A good data scientist knows how to do something really well, but a great data scientist can do "something of everything." From raw data all the way to shining in front of C-level executives, a great data scientist has the skills to architect data systems, build applications, perform modeling and machine learning and wrap up the results in a clear (and quickly iterable) manner. From data models to ETL to databases to distributed algorithms and learning, this book has you covered.
While many resources for Java (and data science) exist, none of them combine the two, and especially not at a level where sophisticated concepts are demonstrated clearly and in simplest terms. Data Science with Java marries the two in a practical way.
Learn an extremely practical set of tools for creating enterprise grade data science applications
Get past the intimidating barrier to machine learning and statistics—and learn how useful object-oriented code can be
Michael Brzustowicz is a physicist turned data scientist. After a PhD from Indiana University, Michael spent his post doctoral years at Stanford University where he shot high powered Xrays at tiny molecules. Jumping ship from academia, he worked at many startups (including his own) and has been pioneering big data techniques all the way. Michael specializes in building distributed data systems and extracting knowledge from massive data. He spends most of his time writing customized, multithreaded code for statistical modeling and machine learning approaches to everyday big data problems. Michael now teaches Big Data, parttime, at the University of San Francisco.
This book is a hot-topic for the Java community that work on issues of Data Science. I don't found any other book like this (Java + Data Science). I liked the use of JavaFX for data visualization, but I missed pure Java Web-based alternatives like Vaadin. The Linear Algebra chapter is very interesting. I think it would be nice to include tools such as Lucene, Solr and Elastic, which are based on Vector Space Model (VSM). In the next chapters I hope to find documented issues about the development of Web services as infrastructure and Deep Learning as a very attractive technique for machine learning.
Bottom Line Yes, I would recommend this to a friend