Data science with java practical methods for scientists and engineers
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Throughout this book, you'll find code examples you can use in your 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. 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. Navigating This Book This book is a logical journey through a data science pipeline. Why I Wrote This Book I wrote this book to start a movement.

This book is the perfect jumping-off point into much deeper waters. 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. The topics covered here will walk you through the data science pipeline, explaining mathematical theory and giving code examples along the way. 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. 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.

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. 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. 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. 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. 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. Java is perhaps the one language that can do it all. . The E-mail message field is required. As data science skyrockets to stardom, fueled by R and Python, very few practitioners venture into the world of 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.

Data Science with Java: Practical Methods for Scientists and Engineers by PhD Michael R. The E-mail message field is required. 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:. Now you can get everything on. 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.

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. 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. Clearly, the tools for data exploration lend themselves to the interpretive languages. Throughout this book, you'll find code examples you can use in your applications. 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. But there is another realm of the engineering—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. 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. A few useful datasets are described in Appendix A. 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.

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. 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. To purchase books, visit Amazon or your favorite retailer. 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. 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. 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. 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.