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Java: Data Science Made Easy

You're reading from   Java: Data Science Made Easy Data collection, processing, analysis, and more

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Product type Course
Published in Jul 2017
Publisher Packt
ISBN-13 9781788475655
Length 734 pages
Edition 1st Edition
Languages
Tools
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Authors (3):
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Alexey Grigorev Alexey Grigorev
Author Profile Icon Alexey Grigorev
Alexey Grigorev
Richard M. Reese Richard M. Reese
Author Profile Icon Richard M. Reese
Richard M. Reese
Jennifer L. Reese Jennifer L. Reese
Author Profile Icon Jennifer L. Reese
Jennifer L. Reese
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Toc

Table of Contents (29) Chapters Close

Title Page
Credits
Preface
1. Module 1 FREE CHAPTER
2. Getting Started with Data Science 3. Data Acquisition 4. Data Cleaning 5. Data Visualization 6. Statistical Data Analysis Techniques 7. Machine Learning 8. Neural Networks 9. Deep Learning 10. Text Analysis 11. Visual and Audio Analysis 12. Visual and Audio Analysis 13. Mathematical and Parallel Techniques for Data Analysis 14. Bringing It All Together 15. Module 2
16. Data Science Using Java 17. Data Processing Toolbox 18. Exploratory Data Analysis 19. Supervised Learning - Classification and Regression 20. Unsupervised Learning - Clustering and Dimensionality Reduction 21. Working with Text - Natural Language Processing and Information Retrieval 22. Extreme Gradient Boosting 23. Deep Learning with DeepLearning4J 24. Scaling Data Science 25. Deploying Data Science Models 26. Bibliography

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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