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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
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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

Data structures and data models

When you have ample of data, but no idea where  It's very important to structure the data, analyze it, and put to good use (wherever needed). In this section, we will be zooming the spotlight on data structures and data models, and also understanding the difference between both.

Data structures

Data developers will agree that whenever one is working with large amounts of data, the organization of that data is imperative. If that data is not organized effectively, it will be very difficult to perform any task on that data, or at least be able to perform the task in an efficient manner. If the data is organized effectively, then practically any operation can be performed easily on that...

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