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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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Toc

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

Hypothesis testing


Hypothesis testing is used to test whether certain assumptions, or premises, about a dataset could not happen by chance. If this is the case, then the results of the test are considered to be statistically significant.

Performing hypothesis testing is not a simple task. There are many different pitfalls to avoid such as the placebo effect or the observer effect. In the former, a participant will attain a result that they think is expected. In the observer effect, also called the Hawthorne effect, the results are skewed because the participants know they are being watched. Due to the complex nature of human behavior analysis, some types of statistical analysis are particularly subject to skewing or corruption.

The specific methods for performing hypothesis testing are outside the scope of this book and require a solid background in statistical processes and best practices. Apache Commons provides a package, org.apache.commons.math3.stat.inference, with tools for performing...

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