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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
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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 process models


Applying data science is much more than just selecting a suitable machine learning algorithm and using it on the data. It is always good to keep in mind that machine learning is only a small part of the project; there are other parts such as understanding the problem, collecting the data, testing the solution and deploying to the production.

When working on any project, not just data science ones, it is beneficial to break it down into smaller manageable pieces and complete them one-by-one. For data science, there are best practices that describe how to do it the best way, and they are called process models. There are multiple models, including CRISP-DM and OSEMN.

In this chapter, CRISP-DM is explained as Obtain, Scrub, Explore, Model, and iNterpret (OSEMN), which is more suitable for data analysis tasks and addresses many important steps to a lesser extent.

CRISP-DM

Cross Industry Standard Process for Data Mining (CRISP-DM) is a process methodology for developing...

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