Data Science with Java Practical Methods for Scientists and Engineers
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
| Publisher Name | OReilly Media |
|---|---|
| Author Name | Hagendorf, Col |
| Format | Audio |
| Bisac Subject Major | COM |
| Language | NG |
| Isbn 10 | 1491934115 |
| Isbn 13 | 9781491934111 |
| Target Age Group | min:NA, max:NA |
| Dimensions | 00.91" H x 00.06" L x 90.00" W |
| Page Count | 236 |
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.
