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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

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Product type Paperback
Published in Jan 2017
Publisher Packt
ISBN-13 9781785280115
Length 386 pages
Edition 1st Edition
Languages
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Authors (2):
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Jennifer L. Reese Jennifer L. Reese
Author Profile Icon Jennifer L. Reese
Jennifer L. Reese
Richard M. Reese Richard M. Reese
Author Profile Icon Richard M. Reese
Richard M. Reese
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Table of Contents (13) Chapters Close

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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