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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
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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

Data science


Data science is the discipline of extracting actionable knowledge from data of various forms. The name data science emerged quite recently--it was invented by DJ Patil and Jeff Hammerbacher and popularized in the article Data Scientist: The Sexiest Job of the 21st Century in 2012. But the discipline itself had existed before for quite a while and previously was known by other names such as data mining or predictive analytics. Data science, like its predecessors, is built on statistics and machine learning algorithms for knowledge extraction and model building.

The science part of the term data science is no coincidence--if we look up science, its definition can be summarized to systematic organization of knowledge in terms testable explanations and predictions. This is exactly what data scientists do, by extracting patterns from available data, they can make predictions about future unseen data, and they make sure the predictions are validated beforehand. 

Nowadays, data science...

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