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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 started out by reviewing the idea of data structures, data models, and databases, and found similarities and differences between them.

Next, we provided an overview of machine learning and related concepts, and then compared the practice of a machine learning statistical project to a database development project.

Finally, we touched on a conceptual use of R and applying machine learning techniques to data from a database.

As this book was aimed at helping the typical data or database developer transition into the world of statistics, we hope the reader has established a sound understanding of the relevant topics in statistics and data science.

Good luck!

 

 

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