Data Science with Java
Practical Methods for Scientists and Engineers
Publisher: O'Reilly Media
Final Release Date: October 2015
Pages: 236

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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
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oreillyData Science with Java
 
4.5

(based on 2 reviews)

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5.0

Well written, concise and taught me a lot

By mj

from palo alto, ca

Comments about oreilly Data Science with Java:

This book taught me a lot about how to use java for data science. Being a data science professional and mainly using Python, I see huge potential to use Java instead

(3 of 3 customers found this review helpful)

 
4.0

This book is a hot-topic for Java Data Scientist

By Julius

from Barcelona, Spain

About Me Developer

Verified Buyer

Pros

  • Concise
  • Easy to understand
  • Helpful examples

Cons

  • Too basic

Best Uses

  • Intermediate
  • Novice
  • Student

Comments about oreilly Data Science with Java:

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.

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