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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 FREE CHAPTER 2. Data Acquisition 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

Sample size determination

Sample size determination involves identifying the quantity of data required to conduct accurate statistical analysis. When working with large datasets it is not always necessary to use the entire set. We use sample size determination to ensure we choose a sample small enough to manipulate and analyze easily, but large enough to represent our population of data accurately.

It is not uncommon to use a subset of data to train a model and another subset is used to test the model. This can be helpful for verifying accuracy and reliability of data. Some common consequences for a poorly determined sample size include false-positive results, false-negative results, identifying statistical significance where none exists, or suggesting a lack of significance where it is actually present. Many tools exist online for determining appropriate sample sizes, each with varying levels of complexity. One simple example is available at https://www.surveymonkey.com/mp/sample-size-calculator...

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