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Java for Data Science

You're reading from Java for Data Science Examine the techniques and Java tools supporting the growing field of data science

Product type Paperback

Published in Jan 2017

Publisher Packt

ISBN-13 9781785280115

Length 386 pages

Edition 1st Edition

Languages

Java

Tools

Deeplearning4j

Concepts

Data Science

Authors (2):

Jennifer L. Reese

Richard M. Reese

View More author details

Table of Contents (13) Chapters

Preface

1. Getting Started with Data Science

2. Data Acquisition FREE CHAPTER

3. Data Cleaning

4. Data Visualization

5. Statistical Data Analysis Techniques

6. Machine Learning

7. Neural Networks

8. Deep Learning

9. Text Analysis

10. Visual and Audio Analysis

11. Mathematical and Parallel Techniques for Data Analysis

12. Bringing It All Together

Chapter 11. Mathematical and Parallel Techniques for Data Analysis

The concurrent execution of a program can result in significant performance improvements. In this chapter, we will address the various techniques that can be used in data science applications. These can range from low-level mathematical calculations to higher-level API-specific options.

Always keep in mind that performance enhancement starts with ensuring that the correct set of application functionality is implemented. If the application does not do what a user expects, then the enhancements are for nought. The architecture of the application and the algorithms used are also more important than code enhancements. Always use the most efficient algorithm. Code enhancement should then be considered. We are not able to address the higher-level optimization issues in this chapter; instead, we will focus on code enhancements.

Many data science applications and supporting APIs use matrix operations to accomplish their...

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Authors (2)

Richard M. Reese

Richard M. Reese

Richard Reese has worked in the industry and academics for the past 29 years. For 10 years he provided software development support at Lockheed and at one point developed a C based network application. He was a contract instructor providing software training to industry for 5 years. Richard is currently an Associate Professor at Tarleton State University in Stephenville Texas. Richard is the author of various books and video courses some of which are as follows: Natural Language Processing with Java. Java for Data Science Getting Started with Natural Language Processing in Java

See other products by Richard M. Reese

Reese

Reese

Jennifer L. Reese studied computer science at Tarleton State University. She also earned her M.Ed. from Tarleton in December 2016. She currently teaches computer science to high-school students. Her interests include the integration of computer science concepts with other academic disciplines, increasing diversity in computer science courses, and the application of data science to the field of education. She has co-authored two books: Java for Data Science and Java 7 New Features Cookbook. She previously worked as a software engineer. In her free time she enjoys reading, cooking, and traveling—especially to any destination with a beach. She is a musician and appreciates a variety of musical genres.

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