Data Science with Java: Practical Methods for Scientists and Engineers
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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. . 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. The E-mail message field is required. 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.

Navigating This Book This book is a logical journey through a data science pipeline. The E-mail message field is required. Chapter 6 provides a quick guide to getting up and running with MapReduce by using customized components suitable for data science algorithms. Java is perhaps the one language that can do it all. Data Science with Java: Practical Methods for Scientists and Engineers by PhD Michael R. Clearly, the tools for data exploration lend themselves to the interpretive languages. 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.

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 Download or or. 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. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. To purchase books, visit Amazon or your favorite retailer. But there is another realm of the engineeringâ€”science hybrid where scale, robustness, and convenience must merge. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline.

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. 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. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. Throughout this book, you'll find code examples you can use in your applications. Chapter 2 addresses the important concept of viewing our data as a matrix. Why I Wrote This Book I wrote this book to start a movement. 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 Download:.

With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. A few useful datasets are described in Appendix A. As data science skyrockets to stardom, fueled by R and Python, very few practitioners venture into the world of Java. Throughout this book, you'll find code examples you can use in your applications. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline.

With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. In Chapter 4, we directly use the concepts from Chapters 2 and 3 to transform our data into stable and usable numerical values. Chapter 5 contains a few useful supervised and unsupervised learning algorithms, as well as methods for evaluating their success. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. In Chapter 1, the many methods for getting, cleaning, and arranging data into its purest form are examined, as are basic data output to files and plotting. 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.

The topics covered here will walk you through the data science pipeline, explaining mathematical theory and giving code examples along the way. Description: 1 online resource 233 pages : illustrations Contents: Copyright; Table of Contents; Preface; Who Should Read This Book; Why I Wrote This Book; A Word on Data Science Today; Navigating This Book; Conventions Used in This Book; Using Code Examples; O'Reilly Safari; How to Contact Us; Acknowledgments; Chapter 1. Now you can get everything on. Throughout this book, you'll find code examples you can use in your applications. From the Preface Who Should Read This Book This book is for scientists and engineers already familiar with the concepts of application development who want to jump headfirst into data science. Author Michael Brzustowicz explains the basic math theory behind e 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.

Abstract: 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. 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. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science p Bookseller: , Greater London, United Kingdom. This book is the perfect jumping-off point into much deeper waters. With this practical book, Java software engineers looking to add data science skills will take a logical journey through the data science pipeline. Throughout this book, you'll find code examples you can use in your applications.

Now that we have data and know what data structure it should take, Chapter 3 introduces the basic concepts that allow us to test the origin and validity of our data. An exhaustive review of matrix operations is presented. If this book inspires you, I hope that you will contribute code to one of the many open source Java projects that support data science. 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. Statistics; The Probabilistic Origins of Data; Probability Density; Cumulative Probability; Statistical Moments; Entropy; Continuous Distributions; Discrete Distributions; Characterizing Datasets; Calculating Moments; Descriptive Statistics; Multivariate Statistics; Covariance and Correlation; Regression; Working with Large Datasets; Accumulating Statistics Merging StatisticsRegression; Using Built-in Database Functions; Chapter 4. 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. 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.

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