Data Science with Java: Practical Methods for Scientists and Engineers
Language: English
Published by O'Reilly Media, 2017
- Softcover
- Used
Seller: Goodwill Southern California, Los Angeles, CA, U.S.A.Goodwill Southern California
AbeBooks seller since December 20, 2017
Condition: Used - Good
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Seller Inventory # LACV.1491934115.G
- Title
- Data Science with Java: Practical Methods for Scientists and Engineers
- Author
- Michael R. Brzustowicz, PhD
- Publisher
- O'Reilly Media
- Publication year
- 2017
- Condition
- good
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1491934115
- ISBN 13
- 9781491934111
Data Science is booming thanks to R and Python, but Java brings the robustness, convenience, and ability to scale critical to today’s data science applications. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. Author Michael Brzustowicz explains the basic math theory behind each step of the data science process, as well as how to apply these concepts with Java.
You’ll learn the critical roles that data IO, linear algebra, statistics, data operations, learning and prediction, and Hadoop MapReduce play in the process. Throughout this book, you’ll find code examples you can use in your applications.
- Examine methods for obtaining, cleaning, and arranging data into its purest form
- Understand the matrix structure that your data should take
- Learn basic concepts for testing the origin and validity of data
- Transform your data into stable and usable numerical values
- Understand supervised and unsupervised learning algorithms, and methods for evaluating their success
- Get up and running with MapReduce, using customized components suitable for data science algorithms
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
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