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Statistics for Data Science

You're reading from   Statistics for Data Science Leverage the power of statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781788290678
Length 286 pages
Edition 1st Edition
Languages
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Author (1):
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James D. Miller James D. Miller
Author Profile Icon James D. Miller
James D. Miller
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Table of Contents (13) Chapters Close

Preface 1. Transitioning from Data Developer to Data Scientist 2. Declaring the Objectives FREE CHAPTER 3. A Developer's Approach to Data Cleaning 4. Data Mining and the Database Developer 5. Statistical Analysis for the Database Developer 6. Database Progression to Database Regression 7. Regularization for Database Improvement 8. Database Development and Assessment 9. Databases and Neural Networks 10. Boosting your Database 11. Database Classification using Support Vector Machines 12. Database Structures and Machine Learning

Summary


In this chapter, we provided an explanation of statistical regularization and then used sample data in an example to illustrate and better understand statistical regularization. Later, we had a discussion of various methods on how to improve (the performance of) data or a data model with regulation. Finally, we saw how well the R language supports the concepts and methods of regulation.

In the next chapter, we're looking to cover the idea of data model assessment and using statistics for assessment. We'll compare the concepts of data assessment and data quality assurance, and finally, apply the idea of statistical assessment to data using R.

 

 

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